Effects That Caffeine Consumption

Effects That Caffeine Consumption Caffeine is the most commonly used psychoactive substance in the United States (Roehrs Roth, 2008). Regular coffee drinkers consume an average of 200-500mg of caffeine per day (Julien, 2005). Caffeine is found in a broad variety of sources including coffee, tea, energy drinks, chocolate and some over the counter medications (Roehrs Roth, 2008). Upon consumption, caffeine reaches peak plasma levels in 30-75 minutes and has a half life of 3-7 hours when consumed in a single dose (Roehrs Roth, 2008). When consumed in greater quantities, the half life is extended (Roehrs Roth, 2008). Caffeines high rate of consumption may be due to the desirable effects it produces, such as increase mental alertness, improved flow of thought and of course, feelings of wakefulness (Julien, 2005). Caffeine is not without its undesirable effects; caffeine consumption may have a negative effect on tasks which require fine motor skills, complex arithmetic skills, or precise timing (Julien, 2005). Structurally, caffeine is similar to adenosine. In the brain, adenosine decreases neural firings and inhibits neurotransmitter release (Roehrs Roth, 2008). Caffeine works as an adenosine antagonist; blocking adenosine receptors in the brain. As a consequence, caffeine prevents adenosine from decreasing neural firings, leading to an increase in firings, and the stimulant effects caffeine is well known for (Roehrs Roth, 2008). Caffeines blocking of adenosine receptors leads to dopamine release in the prefrontal cortex, causing caffeines alerting effects (Julien, 2005). While discontinuation of caffeine consumption may produce withdrawal symptoms, caffeine does not influence the dopaminergic structures associated with rewards and addiction (Julien, 2005). Typical withdrawal symptoms include headache, drowsiness, fatigue, and negative mood (Julien, 2005). It is often difficult to estimate the amount of caffeine a person consumes due to great variability in the amount of caffeine per beverage (particularly coffee), exclusion of new caffeinated products on questionnaires, and variation in consumption from day to day. It is also difficult to compare results between studies due to a great amount of variation in methods of measuring caffeine consumption levels (Shohet Landrum, 2001). A study by Shohet Landrum (2001) of undergraduate university students implemented the use of an updated version of the caffeine consumption questionnaire as well as looking at chronotype and age. The caffeine Consumption questionnaire decreases a great deal of inaccuracy of caffeine consumption measurement. Shohet Landrum (2001) found that the average participant in the study consumed 1597.6mg/week. They also found that level of caffeine consumption is positively correlated with age. It was speculated that this increase may be an effort to compensate for de creased metabolism and subsequent decrease in energy (Shohet Landrum, 2001). In the same study, there was no significant difference in caffeine consumption between males and females (Shohet Landrum, 2001). Caffeine consumption in the evening was higher among older people, who tended to be morning-types (Shohet Landrum, 2001). The effects that caffeine consumption has on sleep are vast. Orbeta, Overpeck, Ramcharrin, Kogan Ledski (2006) found in a study of American high school students that those who reported a high rate of caffeine consumption also reported more difficulty falling asleep and felt more tired in the morning. In a number of studies, caffeine administration in varying amounts significantly reduced total sleep time and increased sleep onset latency (Roehrs Roth, 2008). Some studies also found a reduction in percentage of slow wave sleep after caffeine administration (Roehrs Roth, 2008). In a study where caffeine was administered prior to sleep, EEG spectral power density was reduced in the .75 4.5 Hz band. In a parallel study, men were administered 200 mg of caffeine upon waking (07:00 h) still experienced a reduction in EEG spectral power density in the .75 4.5 Hz range in the subsequent night sleep (Landolt, Werth, Borbely, Dijk, 1995). In this same study, total sleep time and sleep eff iciency were reduced following caffeine administration in the morning. Power density was reduced in the .25 .5 Hz range, and enhanced in the 11.25 12.00 Hz and 13.25-14.00 Hz ranges for NREM sleep (Landolt et al., 1995). Though a single 200 mg dose of caffeine in the morning clearly influences sleep propensity and power density of the EEG in the subsequent sleep episode, there was no deterioration in subjective sleep quality, and there is not a severe disruption of sleep continuity (Landolt et al., 1995). In contrast, a study by Sanchez-Ortuno, Moore, Taillard, Valtat, Leger, Damien, Bioulac, and Philip (2005) found that up to eight cups of coffee consumed by regular coffee drinkers was not associated with reduced TST. There was also no relationship found between caffeine consumption and day time sleepiness in participants consuming up to eight cups daily (Sanchez-Ortuno et al., 2005). The chronotype of an individual may be related to caffeine consumption. Chronotypes are a preference for being active during a particular time of day (Giannotti, Cortesi, Sebastiani, Ottaviano, 2002). Some individuals may be categorized as Morning-Types. Morning Types prefer to wake early in the morning, retire earlier in the evening, and are most active in the early hours of the day, where as Evening-Types prefer to rise later, and engage in activities later in the day. Others may fall somewhere between the morning-type and evening-type extreme. Daily physiological rhythms such as core body temperature, blood pressure and hormone secretions vary from one chronotype to another. Morningness and Eveningness also tend to vary with age, with older adults generally demonstrating a preference for morning activity, and younger adults a preference for evening activity (Giannotti et al., 2002). A study by Giannotti et al. (2002) of adolescents found that as they approached young adulthood, t heir circadian preference shifted more towards Eveningness. Giannotti et al. (2002) also found that Evening types tended to consume more caffeine, particularly in the morning. This may be due to forced pressure to adhere to a schedule more appropriate for those with a preference for morning activity (Giannotti et al., 2002). In a study of both men and women with different, but fixed work schedules by Ana Aden (1994) it was found that caffeine consumption increased with preference for evening. Evening types consumed more caffeine than neutral types, and neutral types consumed more caffeine than morning types. Interestingly, a large percentage of evening types were found to be caffeine abusers. 500 mg or more of caffeine per day was considered abuse (Aden, 1994). Adolescent evening types showed a more irregular sleep schedule and poorer subjective sleep quality in a study by Giannotti et al. (2002). Evening types also had higher sleep/wake behaviour scores than morning types, an indication of more sleep problems in evening types (Giannotti et al., 2002). Evening type adolescents reported consuming more sleeping pills than morning types as well as more day time sleepiness (Gianotti et al., 2002). Evening types had a greater tendency to fall asleep at school, and attention problems as well (Giannotti et al., 2002). An increase in the accessability of technology like computers, internet, television, and MP3 players may also impact caffeine consumption as well as sleep. A study by Calamaro, Mason, Radcliffe (2009) found that adolescents with higher scores on the multi-tasking index also reported higher caffeine intake, increase daytime sleepiness, increased incidents of falling asleep at school, and decreased total sleep time. Only 20% of the teenagers in this study received the recommended 8-10 hours of sleep for their age (Calamaro et al., 2009). 33% reported falling asleep at school on a regular basis, and 37% and 42% take naps on school days and weekends respectively (Calamaro et al., 2009). Clearly there is a great deal of interaction between caffeine consumption and chronotype. There is also apparent interaction between caffeine consumption and sleep quality. Chronotype had an influence on sleep quality in adolescents, There is also a relationship between caffeine consumption and sleep quality and multi-tasking/technology use. The present study aimed to examine the interrelationship between these variables in a group of university students. It was hypothesized that students who reported higher caffeine consumption would report lower subjective sleep quality. This relationship would be demonstrated by a significant positive correlation between level of caffeine consumption determined by Caffeine Consumption Questionairre (mg/week) (Modified from Landrum, 1992) and score on the Pittsburgh Sleep Quality Index (a higher score indicates poorer sleep quality) (Buysse et al., 1989). It was also predicted that students who were evening-types would consume a greater amount of c affeine than morning-type students. This would be demonstrated by a significant negative correlation between Morningness-Eveningness Questionairre (a lower score indicates a preference for eveningness) (Horne stberg, 1976) and daily caffeine consumption (mg/week) . Next, it was predicted that evening types would experience more subjective sleep problems than morning types. More specifically, there would be a significant negative relationship between scores on the Morningness-Eveningness Questionnaire and Pittsburgh Sleep Quality Index score. The fourth prediction was that students who scored higher on the Nighttime Activities (Multi-tasking) Index would also consume a greater amount of caffeine. Specifically, there would be a positive relationship between Caffeine Consumption Questionnaire score and Nighttime Activities (Multi-Tasking) Index score. Finally, we predicted that students who were evening-types would use more technology between 21:00 and 06:00. This would be indicated b y a significant negative relationship between Morningness-Eveningness score and Nighttime Activities (Multi-Tasking) Index score. Method Participants Participants in this study were 49 undergraduate students enrolled in a Sleep and Arousal course and Trent University. Student age ranged from 20-31 years. Mean age of participants was 22.12 years (SD 2.26). 9 males and 39 females participated in this study. Materials Materials used were 4 established questionairres. The Morningness-Eveningness Questionairre (Horne stberg, 1976) was used to determine an individuals chronotype (preferred or peak time of day (morning, evening or neutral)). Scores range from 16-86. Questionnaires were scored as follows: (16-30) Definitely Evening, (31-41) Moderately Evening, (42-58) Neutral, (59-69) Moderately Morning, (70-86) Definitely Morning. The Pittsburgh Sleep Quality Index was used to measure students overall sleep quality (Buysse et al. 1989). Scores range from 0-21, with lower scores indicating better sleep quality. A modified version of the Caffeine Consumption Questionairre (Landrum, 1992) was used to estimate weekly caffeine consumption in students. Participants indicate how much caffeine they consume in the morning, afternoon, evening, and night time. Students also indicate the source of caffeine (small coffee, medium tea, soft drink, large coffee). The caffeine content of each type and size of drink was determined by Calamaro et al. (2009) and Roehrs and Roth (2008). Finally, the Night-Time Activities Questionnaire, modified from Calamaro et al. (2009) was used to measure the amount of time students spent doing various technology based activities in the evening (9:00pm 6:00am). Activities such as watching television, and using the computer were included). A multi-tasking index was then created by adding the total hours of time spent on all tasks and dividing this number by 9 (the total hours between 9:00 pm and 6:00 am). A student who engages in 9 hours of activity in that 9 hour period would receive a score of 1.0 (A score greater than 1 is possible, for example, if a student was listening to music and using the computer at the same time). Procedure Participants filled out all four questionnaires during a scheduled lecture period. The Morningness-Eveningness Questionnaire and the Pittsburg Sleep Quality Index were scored by students after completion, while the other two questionnaires were scored by the instructor. Results Caffeine Consumption Questionairre The mean level of caffeine consumption in milligrams per week for the morning (06:00 12:00) period was 685.63 (SD = 1032.21). Mean afternoon (12:00 18:00) period caffeine consumption was 394.90 (SD = 554.39). The mean level of evening (18:00 02:00) period caffeine consumption in these university students was 320.49 (SD = 355.48) and mean night time (02:00 06:00) caffeine consumption was 24.84 (SD = 64.49) milligrams per week. Mean caffeine consumption total in milligrams per week was 1425.86 (SD = 1737.82). These results were similar to results found by Shohet et al. in that the greatest amount of caffeine was being consumed in the morning time. There was a slightly lower level of total caffeine consumption in our study compared to the results found by Shohet et al., with a difference of 171.74 mg/week between the two studies. This amount is equivalent to about 1 cup of coffee. (MORE COMPARISON BETWEEN OURS AND SHOHET..SEE TABLE 2 IN PAPER AT BATA) The mean source of the caffeine consumed weekly in milligrams was 974.69 (SD = 1713.09) for coffee, 270.12 (SD = 338.18) for tea, 99.24 (SD = 163.39) for soft drinks, 45.06 (SD = 127.23) for energy drinks, and 36.73 (SD = 74.44) for hot chocolate. The vast majority of caffeine consumed weekly by these university students was via coffee while very little caffeine was consumed in hot chocolate. Morningness-Eveningness Questionnaire (MEQ) The mean MEQ score was 43.59 (SD = 12.25). Scores ranged from 24 to 69. 16.33% of participants were Definitely-Evening (n= 8), 34.69% were Moderately-Evening (n=17), 36.73% were Neutral (n=18) and 12.24% were Moderately-Morning. None of the participants were Definitely-Morning types. Pittsburgh Sleep Quality Index (PSQI) Each subscale of the PSQI has a possible score of 0-3. The mean Subjective Sleep Quality score was 1.37 (SD = 0.83). The mean Sleep Onset Latency score was 1.84 (SD = 1.01). The mean Sleep Duration score was 0.78 (SD = 0.82). The mean Habitual Sleep Efficiency score was 0.69 (SD = 0.98). The mean Sleep Disturbances score was 1.55 (SD = 1.14). The mean Use of Sleeping Medication was 0.37 (SD = 0.83), and the mean Daytime Dysfunction score was 1.35 (SD = 0.83). The mean total score on the PSQI was 7.78 (SD = 3.93). According to Buysse et al. (1988), a score greater than 5 indicates that someone is a poor sleeper. The mean score of our participants was within the range of abnormal. The greatest amount of sleep disturbance came from high sleep onset latency, while the least disruptive factor was reliance on the use of sleep medications. Night-Time Activities Questionnaire (NTAQ) The mean data for the activities included on the NTAQ are included in figure 1. The mean multi-tasking index of these night time activities is 0.60 (SD = 0.29). The range of multi-tasking index scores was 0.12 1.39. A score of 0.60 means that the participant was doing some combination of the activities on the NTAQ for 5.40 hours. (0.60 x 9 hours = 5.40) of the 9 hour sleep period. In the case of the score of 1.39, the participant was engaging in an activity on the NTIQ for 12.51 hours. Since the measured period is only 9 hours, this participant was engaging in more than one activity at a time, for example, listening to MP3 player and online computer use. Results of Correlation Analysis There was a significant negative correlation between MEQ and Multi-Tasking Index. Morning types tended to have lower Multi-Tasking Index scores than Evening types, r = -.32, p Table 1 Correlations found between Morningness-Eveningness Questionnaire (MEQ), Pittsburg Sleep Quality Index (PSQI), Multi-tasking Index, and Caffeine Consumption Questionnaire. . Â ­_ MEQ PSQI Multi-Tasking . MEQ score -.16 -.32* PSQI score .03 Caffeine Consumption Coffee -.06 .31* -.06 Tea .20 -.20 -.08 Hot Chocolate .13 -.18 .08 Soft Drinks -.30* .02 .08 Energy Drinks -.14 .20 .07 . Total Caffeine -.06 .25 .01 . * p Discussion We predicted that participants who consumed a greater level of caffeine would have higher scores, indicating poorer sleep quality, on the Pittsburgh Sleep Quality Index. Although total caffeine consumption level failed to predict a higher sleep quality score, there was a significant negative correlation between level of coffee consumption and PSQI. Morningness-Eveningness Questionnaire Score was predicted to negatively correlate with score on the Caffeine Consumption Questionnaire. Total caffeine consumption did not significantly correlate with MEQ score. Level of caffeinated soft drink consumption did significantly correlate with MEQ with evening types consuming greater amounts of caffeinated soft drinks than morning-types. It was predicted that evening types would report more sleep problems via the PSQI. This correlation failed to reach significance in our analysis. There is no significant difference between Pittsburgh Sleep Quality Index score in evening-types from morning-types. We predicted that students who scored higher on the Nighttime Activities (Multi-tasking) Index would also consume a greater amount of caffeine. The analysis revealed no significant relationship between these variables. Our final prediction was that evening-types would engage in a greater level of technology use in the evening, as indicated by a significant negative relationship between MEQ score and Multi-Tasking Index. There was a significant relationship between MEQ and Multi-Tasking Index. Evening types did tend to engage in more activities involving technology between the hours of 2100 and 0600 than morning-types, as predicted. Using The Caffeine Consumption Questionnaire and Pittsburgh Sleep Quality Index as a measure, consumption of higher levels of caffeine did not did predict poorer sleep quality. Although several studies found that caffeine consumption increased sleep onset latency, decreased total sleep time and increased daytime sleepiness, we did not find that high levels of total caffeine consumption predicted a significantly poorer sleep quality score (Roehrs Roth, 2008). Although total caffeine consumption and PSQI were not correlated, caffeinated coffee consumption did predict a poorer sleep quality score. This contrasts findings by Sanchez-Ortunga et al. (2005) in which up to eight cups of coffee consumed by regular coffee drinkers did not result in a significantly lower TST. Although it should be taken into consideration that TST is only one component of the PSQI. Contrary to our findings, Gianotti et al. (2002) found that Evening-types tended to consume a greater amount of caffeine than morning types. Ana Aden (1994) also found that daily caffeine consumption increased as preference for evening activity increased. Although these results contrast our findings, we did find a slight but significant relationship between consumption of caffeinated soft drinks and preference for evening. Gianotti et al. (2002) also found that evening-type adolescents reported poorer subjective sleep quality than morning types. These evening-type adolescents also showed a more irregular sleep schedule. Evening types showed greater daytime sleepiness, increased frequency of falling asleep during the day, and other indications of poor sleep quality (Gianotti et al., 2002). Contrary to these findings, we found no relationship between PSQI score and chronotype. Although Calamero et al. (2009) found that those reporting an increased multi-tasking index score also consumed greater amounts of caffeine, we found no relationship between the two. We did, however, find a significant relationship between chronotype and multi-tasking index. Evening types tended to engage in more technologically based activities between 2100 and 0600. There was no previous research available examining the relationship between chronotype and Night-time Activities/Multi-tasking Index. This may be a possible area of further investigation. One limitation of this study is the lack of diversity in the sample. The participants were a relatively small group of undergraduate psychology students between the age of 20-31. The small sample size may have made it difficult for trends in the data to reach significant levels. Also, chronotype and caffeine consumption have been shown to change over the lifetime, however, we were able to examine only a small window of young adulthood, leaving little opportunity for drastic variations. Also, being students, many of these participants may have schedules which vary drastically from day to day, as well as an increased frequency of engaging in late night activities with peers. These behaviours may have a confounding influence on many sleep variables. Thus, these findings may not be generalized to the population. Re-examining the same material with a larger and more diverse sample may yield more helpful results. This would be fairly simple to do since the questionnaires may be filled out with little guidance or instruction, and simply be distributed and returned by post or electronically administered. Another limitation is that the entire data collection procedure relied completely on student self-reports. The accuracy of these self-evaluations of sleep quality, sleep latency, and level of caffeine consumption may not have been accurate. Some questionnaires were also self scored, leaving open the opportunity for error in calculations. Although much of our analysis of caffeine consumptions effect on sleep quality failed to reach statistical significance, the trends in the data indicate that caffeine does likely detrimentally influence sleep quality. As previous research has shown, the impact caffeine may have on daytime functioning and sleep may be greater than many people realize. Caffeine consumption may be leading to a poorer nights sleep, and this less recuperative sleep subsequently may lead to more caffeine consumption the following day to compensate for the caffeine disrupted sleep of the night before. One can see how this may result in a caffeine/poor sleep cycle. Another interesting finding was the correlation between chronotype and Multi-tasking index score. It would be interesting to investigate whether this relationship is due to evening-types engaging in more night-time activities in order to simply occupy the time between when they believe they should be sleeping and when they are able to sleep, or if the opportunity to occupy the mind and stave off sleep, and disrupting their natural activity time preference. Although we did not specifically make any predictions regarding Multi-tasking Index and PSQI, it is interesting to note that there was no relationship between Multi-tasking Index and PSQI. Research by Calamaro et al. (2009) found that a high Multi-tasking Index was related to sleep problems like difficulty falling asleep, decreased total sleep time and daytime sleepiness. There was no relationship between chronotype and sleep quality in our study, despite findings of a significant relationship by Gianotti et al. (2002). Although the trend in our data leaned towards a similar relationship, it did not reach significance. The difference in our findings compared to Gianotti et al. (2008) may have to do with factors unique to adolescents. In summary, there is a significant relationship between Multi tasking and chronotype, PSQI and coffee consumption level. All other comparisons failed to reach significance. The trend in the data indicate that caffeine does indeed detrimentally effect sleep quality, but the degree of influence it has remains unclear. References Adan, A. (1994). Chronotype and personality factors in the daily consumption of alcohol and psychostimulants. Addiction, 89(4), 455-462. Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., Kupfer,D.J. (1989). The Pittsburgh Sleep Quality Index (PSQI): A new instrument for psychiatric research and practice. Psychiatry Research, 28(2), 193-213. Calamaro, C.J., Mason, T.B., Ratcliffe, S.J. (2009). Adolescents living the 24/7 lifestyle: effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics, 123(6), 1005-1010. Gianotti, F., Cortesi, F., Sebastiani, T., Ottaviano, S. (2002). Circadian preference, sleep and daytime behaviour in adolescence. Journal of Sleep Research, 11(3), 191- 199. Julien, R.M. (2005). Caffeine and nicotine. In A primer of drug action. (10th ed., pp. 225-251). New York: Worth Publishers. Landolt H.P., Werth, E., Borbely, A.A., Dijk, D.J. (1995). Caffeine intake (200 mg) in the morning affects human sleep and EEG power spectra at night. Brain Research, 675(1-2), 67-74. Landrum, R.E. (1992). College students use of caffeine and its relationship to personality. College Student Journal, 26(2), 151-155. Orbeta, R.L., Overpeck, M.D., Ramcharran, D., Kogan, M.D., Ladsky, R. (2006). High caffeine intake in adolescents: associations with difficulty sleeping and feeling tired in the morning. Journal of Adolescent Health, 38(4), 451-453. Roehrs, T., Roth, T. (2008). Caffeine: Sleep and daytime sleepiness. Sleep Medicine Reviews, 12(2), 153-162. Sanchez-Ortuno, M., Moore, N., Taillard, J., Valtat, C., Legar, D., Bioulac, B., Philip.,P. (2005). Sleep duration and caffeine consumption in a French middle-aged working population. Sleep Medicine, 6(3), 247-251. Shohet, K.L., Landrum, R.E. (2001). Caffeine consumption questionnaire: a standardized measure for caffeine consumption in undergraduate students. Psychology Reports

Nurse Shortage Approaches Essay

When nurses are forced to work with high nurse to patient ratios, patients can develop a variety of infections, get injured, and can lead to death. Often at times patients are discharged home too soon without adequate education about how to manage their illness or injury (Raquel & Sean, 2011). Because of patients being discharged to soon, this causes them to return back to the hospital often sicker than they were before. Increase in rate of admissions, transfers, and discharges on hospital unit’s raises nurses’ workload. When nurses have fewer patients, they are able to provide high quality care (Raquel & Sean, 2011). A quantitative research was done based on knowledge of unit’s attribute and shift by shift nurse staffing levels. The researchers inspected 43 units of medical and surgical patient’s mortality in an infamous magnet hospital here in United States. The units and shifts staffing data from 2003 to 2006 were obtained and consolidated with patient data resulting in 3.2 million unit shifts for 197961 patients. The outcome of two staffing variables were scrutinize using a shift unit level: understaffing actual registered nurses staffing eight hours or more below target staffing levels generated by a patient classification system and high turnover which means unit admissions, transfers and discharges exceeds mean day shift by one standard deviation. Patient survival rate was analyzed using Cox proportional hazard regression models with adjustment for clients, unit and shifts risk covariates was practiced. The risk adjust mortality was evaluated to staffing and turnover within the first 5 to 30 days after admission and during previous shifts. The result was dangerous ratios (Raquel & Sean, 2011). The result shows that each shift and unit that where understaffed by 4 to 7% and also has high turnover the risk of patient’s death increases by 2 to 5%. The authors also pointed out that low acuity patient on units that are understaffed, the risk of death is 4 to 12% and with high turnover the risk of death is 7 to 15% (Raquel & Sean, 2011). The strength of the study is that the researcher was able to analyzed  patients in the units, staffing, shifts levels and turnover. The authors pointed out that the limitation in the study is that the patient sample was not identical; some confounding of staffing decision with patient clinical conditions and differentials in staffing could have influence the study. The authors elaborated that the findings in this study is consistent with previous association between registered nurse coverage and lower hospital mortality rate, (Raquel & Sean, 2011). Contrast and Compare Nursing Leaders Sigma Theta Tau International (STTI) and 40 other healthcare groups of organizations and leaders are helping to find solutions on the war of nurse shortage. The affiliation designed a website, made advertising movements and secured media footage (Sigma Theta Tau International). These measures were being done as a way to entice young people to enter the nursing profession. The advertisement measures also helped to spread the word to the public of the drastic need for nurses. STTI has risen over $1 million dollars to help towards the campaign of nurse shortage. The campaign continues to receive new coverage nationwide to raise awareness of the nurse shortage (Sigma Theta Tau International). Johnson & Johnson and The Honor Society of Nursing worked together and contribute $20 million a year towards campaign to scale down on nurse shortage. The goal of Johnson & Johnson and The Honor Society campaign is to attract people to work in hospitals and extended care facilities (Sigma Theta Tau International). August 1, 2002 the former president Bush, signed Nurse Reinvestment Act. The Nurse Reinvestment Act is intended to alleviate the nursing shortage by offering incentives with tuition reimbursements. The Nurse Reinvestment Act details five main functions: Scholarship for future nursing students with loan reimbursement programs. Public business announcement to entice people to enroll in nursing programs (ANA 2015). Career advancements programs for workers that would like to further their profession. Awarding grants to administrators for magnet programs. More focus on gerontology programs by offering grants for long-term care training. Fast track staffing reimbursement programs for those who decide to teach nursing curriculum (ANA 2015). Contrast and Compare Nursing Managers Mangers can help decrease nurse shortage by increasing the staff morale on the units. New nurses often feel intimidated; some are giving tough assignments that they may need help with. As a manger overlooking is essential to make sure the work load is evenly distributed (ANA 2014). Management by Walking around (MBWA) is a manger that is always on the move making their rounds. When making rounds focus on what the staff is doing, offer help if needed, interact with the family members and the staff. This type of manger is in long-term care facilities. By making rounds you can ensure that all staff is being treated fairly, you are able to see what works and does not work (ANA 2014). My Personal and Professional Leadership Style As a nursing leader I prefer Transformational Leadership style. With transformational leadership the focus is on motivating and constructing relationships among the staff, so that the same mission and vision can be obtained. Transformational are good communicators, they use their charm to get people to see the perspective on situations. Praise and encouragement is often used by transformational leaders, I am also a great problem solver both at work and at home. As a nurse manager my approach would be to continue community with the staff and encourage the staff through humanizing nursing theory. A good manager will treat their employees with a holistic approach. It is not every day that the workers are working up to 100%; they could be having a personal problem. Continue to be assertive when needed, confront and deal with conflicts as they arise. When you treat your employees with respect, dignity, and allow autonomy they will be willing to work hard at their jobs. Summary Hospitals used both per diem nurses and traveling nurses who sign short-term contracts to fill individual shifts and accommodate short-term staffing needs arising from staff vacations or medical leaves. Some hospitals used internal staffing agencies or float pools. The downsides to these strategies include high cost and decreased quality of care. Hospitals looking for more long term strategies are investing more in nurse education, lower nurse to patient ratios or limiting volume of patients so not to overload available staff. Hospitals are also partnering with nursing schools  in an effort to grow population of new nurses and possibly secure contracts with students who attend clinical at their facilities. Nursing shortage can lead to increase patient harm and decrease in access to quality care. The nursing shortage is not a quick fix, but acknowledgment of the problem is a beginning to a solution. It is imperative that health care facilities staff their units properly so that the patients will receive quality care that they deserve. Understaffed and high turnover shifts increase the risk of death. References American Nurses Association (ANA 2014). Nursing leadership, management and leadership style. Retrieved on April 22, 2015 from. http://www.aanac.org/docs/white-papers/2013-nursing-leadership—management-leadership-styles.pdf?sfvrsn=4 American Nurses Association (ANA 2015). Nurse reinvestment act background. Retrieved on April 22, 2015 from. http://www.nursingworld.org/NurseReinvestmentAct.aspx Raquel, M., & Sean, C. (2011). Staffing with nurse understaffing and high patient churn linked to heightened inpatient mortality risk in a single site study. Evidence based nursing, Vol. 14, p122-123. Retrieved on April 22, 2015 from. http://dx.doi.org/10.1136/ebn.2011.100052 Sigma Theta Tau International (1199-2015). Honor Society of Nursing. Facts on the nursing shortage in North America. Retrieved on April 22, 2015, from. http://www.nursingsociety.org/Pages/default.aspx

Ways to Prevent School Shootings - 1946 Words

School shootings are terrifying to think about, but there are ways to help prevent the massacres from ever happening again. The first known school shooting was at the Texas Tower at the University of Texas in 1966 where Charles Whitman shot and killed 16 people while injuring 31 others. Who would have known since that date that we would have more then 200 deaths on school campuses? The most storied shooting in the 90’s was probably the Columbine massacre where on April 20, 1999 Dylan Klebold and Eric Harris killed 32 students and faculty before turning the guns on themselves. The horror scene from the day will always be remembered in many lives and will continue over the years. There were many other horrible shootings over the years as†¦show more content†¦Noticing some early warning signs and not being scared to report them can help someone from making the biggest mistake of their life and saving people from harms way should start with the love shown from the parents . Having the parents be in the kids life knowing his or her surroundings at all time can be the key for prevention. Showing our kids love and attention will not make them want to do harm to others and having your child talk to you if something is bothering them. When you aren’t in the child’s life and not knowing what’s going on with them could easily make them feel lost inside and can turn to depression like our parents don’t care about them, a key fact is no matter how busy our lives with our job and anything else that we might do in our spare time parents should always find time to spend with our child each day, even if its only for a half hour, each time that we show love can be a key for our child not to do the unthinkable. The key facts from kids that do the shooting is a lot of times from being bullied at school or make fun of that will keep there anger all bottled up inside until one day the anger explodes and the worst scenario happens and that i s a school shooting. Parents should also talk to their child to let them know not to be scared to report things that might seem out of the ordinary. Kids should always report to authority or a teacherShow MoreRelatedSchool Shootings : Causes And Consequences Of School Shootings895 Words   |  4 Pages School Shootings â€Å"An average of 9,289 people shot dead by a gun, or 774 a month, 178 a week, 25 a day, or a little more than one per hour,† statistics from (Sandy Hook Effect Articles.) School shootings are one of the most tragic events, from Columbine in 1999 to Sandy Hook in 2012, to many more recent ones today. Colleges to high schools and even to elementary schools, there is always a threat and a possibility of having a shooting. 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The Crime Of Criminal Justice - 1547 Words

When it Comes to Criminals Violence and crime have been splattered on the pages of history for centuries. Sadly, however, they will be part of the future as well. Is being a police officer or investigator as cool as they make it seem on television? Criminal Minds is just one of the many investigative television shows that keep its audience pondering over the reality of crimes. Criminal Minds portrays the psychologist as having a more active role than they really do, explained Marc T. Zucker, academic chair of the undergraduate School of Criminal Justice at Kaplan University, in one article. We all love the thrill of the chase and arrest, however, psychologists don t typically accompany officers in the apprehension of suspects. Still as†¦show more content†¦This type of list would include content such as age, gender, personality, habits, location, and so forth (Scottsdale). As vague as this information may seem, it will not only help to locate the criminal, but it can also help solve crimes as well. Br ian Campbell, a probation officer from the United States Probation Office, says that this job will require a criminal justice degree, an additional psychology degree, and a bachelor s degree. Campbell also mentioned that some specific jobs will require an individual to have a master s degree or be working to receive said degree. Furthermore, apart from degrees, this specific career field will require very good communication and people skills as well. Campbell mentions that the most difficult part of his job is explaining to the family why the offender is either going to jail or receiving disciplinary actions. This can be especially hard when there are children involved because the last thing the officer wants to do is spoil the relationship between the child and guardian. Leading to the topic of crime, there is an unlimited amount of resources full of statistics and crime rates. Preliminary figures indicate that law enforcement agencies throughout the nation showed an overall increase of 1.7 percent in the number of violent crimes brought to their attention for the first six months of 2015 when compared with figures reported for the same time in 2014 (FBI). In 2014, there were 3,961