How our longitudinal employment patterns might shape our health as we approach middle adulthood-US NLSY79 cohort.

Recent labor market transformations brought on by digital and technological advances, together with the rise of the service economy since the 1980s, have subjected more workers to precarious conditions, such as irregular work hours and low or unpredictable wages, threatening their economic well-being and health. This study advances our understanding of the critical role employment plays in our health by examining how employment patterns throughout our working lives, based on work schedules, may shape our health at age 50, paying particular attention to the moderating role of social position. The National Longitudinal Survey of Youth-1979 (NLSY79), which has collected 30+ years of longitudinal information, was used to examine how employment patterns starting at ages 22 (n ≈ 7,336) might be associated with sleep hours and quality, physical and mental functions, and the likelihood of reporting poor health and depressive symptoms at age 50. Sequence analysis found five dominant employment patterns between ages 22 and 49: "mostly not working" (10%), "early standard hours before transitioning into mostly variable hours" (12%), "early standard hours before transitioning into volatile schedules" (early ST-volatile, 17%), "mostly standard hours with some variable hours" (35%), and "stable standard hours" (26%). The multiple regression analyses indicate that having the "early ST-volatile" schedule pattern between ages 22 and 49 was consistently, significantly associated with the poorest health, including the fewest hours of sleep per day, the lowest sleep quality, the lowest physical and mental functions, and the highest likelihood of reporting poor health and depressive symptoms at age 50. In addition, social position plays a significant role in these adverse health consequences. For example, whereas non-Hispanic White women reported the most hours of sleep and non-Hispanic Black men reported the fewest, the opposite was true for sleep quality. In addition, non-Hispanic Black men with less than a high school education had the highest likelihood of reporting poor health at age 50 if they engaged in an employment pattern of "early ST-volatile" between ages 22 and 49. In comparison, non-Hispanic White men with a college degree or above education had the lowest likelihood of reporting poor health if they engaged in an employment pattern of stable standard hours. This analysis underscores the critical role of employment patterns in shaping our daily routines, which matter to sleep and physical and mental health as we approach middle adulthood. Notably, the groups with relatively disadvantaged social positions are also likely to be subject to nonstandard work schedules, including non-Hispanic Blacks and people with low education; hence, they were more likely than others to shoulder the harmful links between nonstandard work schedules and sleep and health, worsening their probability of maintaining and nurturing their health as they approach middle adulthood.

The development and implementation of the district nursing Welsh Levels of Care Tool.

District nurses provide a vital service to individuals and their families and carers in the community. With increasing complexity of care and changes in the needs of the UK population, developing evidence-based workforce and workload tools which are fit for purpose is essential. This article describes the development, piloting and implementation of the District Nursing Welsh Levels of Care (DN WLoC), acuity and dependency tool which has been designed to be used alongside nurses professional judgment, to evidence the acuity and dependency of patients, and to help inform decisions of nurse staffing requirements in district nursing services. The initial pilot successfully achieved its objective and demonstrated that the draft DN WLoC tool is applicable in practice by district nurses across Wales, with limited but positive findings in the reliability and validity of the WLoC tool when applied in clinical practice.

Hospital Performance on Hospital Consumer Assessment of Healthcare Providers and System Ratings: Associations With Nursing Factors.

OBJECTIVE: To determine which hospital nursing resources (staffing, skill mix, nurse education, and nurse work environment) are most predictive of hospital Hospital Consumer Assessment of Healthcare Providers and System (HCAHPS) performance. BACKGROUND: HCAHPS surveying is designed to quantify patient experience, a measure of patient-centered care. Hospitals are financially incentivized through the Centers for Medicare and Medicaid Services to achieve high HCAHPS ratings, but little is known about what modifiable hospital factors are associated with higher HCAHPS ratings. PATIENTS AND METHODS: Secondary analysis of multiple linked data sources in 2016 providing information on hospital HCAHPS ratings, hospital nursing resources, and other hospital attributes (eg, size, teaching, and technology status). Five hundred forty non-federal adult acute care hospitals in California, Florida, New Jersey, and Pennsylvania, and 11,786 registered nurses working in those hospitals. Predictor variables included staffing (ie, patient-to-nurse ratio), skill mix (ie, the proportion of registered nurses to all nursing staff), nurse education (ie, percentage of nurses with a bachelor's degree or higher), and nurse work environment (ie, the quality of the environment in which nurses work). HCAHPS ratings were the outcome variable. RESULTS: More favorable staffing, higher proportions of bachelor-educated nurses, and better work environments were associated with higher HCAHPS ratings. The work environment had the largest association with higher HCAHPS ratings, followed by nurse education, and then staffing. Superior staffing and work environments were associated with higher odds of a hospital being a "higher HCAHPS performer" compared with peer hospitals. CONCLUSION: Improving nursing resources is a strategic organizational intervention likely to improve HCAHPS ratings.

A qualitative study on the adoption of the new duty hour regulations among medical residents and faculty in Korea.

Duty hour regulations (DHRs) were enforced in 2017 in Korea to prevent the detrimental effects of excessively prolonged working hours among medical residents. We investigated the adoption of and implications of the new DHRs among medical residents and faculty members. Semi-structured interviews were conducted with 15 medical residents and 9 faculty members across general surgery, internal medicine, obstetrics-gynecology, and pediatrics departments at Chonnam National University Hospital. Based on the constructivist grounded theory, we developed themes from the data by concurrent coding and analysis with theoretical sampling until data saturation. In addition, respondent validation was used to ensure accuracy, and all authors remained reflexive throughout the study to improve validity. The methods of DHRs adoption among residents and faculty members included the following 4 themes: DHRs improved work schedule, residents have more time to learn on their own, clinical departments have come to distribute work, organization members have strived to improve patient safety. Residents have undertaken initial steps towards creating a balance between personal life and work. Teamwork and shift within the same team are the transitions that minimize discontinuity of patient care considering patient safety. Teaching hospitals, including faculty members, should ensure that residents' work and education are balanced with appropriate clinical experience and competency-based training.

Staffing and Antipsychotic Medication Use in Nursing Homes and Neighborhood Deprivation.

Importance: Inappropriate use of antipsychotic medications in nursing homes is a growing public health concern. Residents exposed to higher levels of socioeconomic deprivation in the area around a nursing home may be currently exposed, or have a long history of exposure, to more noise pollution, higher crime rates, and have less opportunities to safely go outside the facility, which may contribute to psychological stress and increased risk of receiving antipsychotic medications inappropriately. However, it is unclear whether neighborhood deprivation is associated with use of inappropriate antipsychotic medications and whether this outcome is different by facility staffing levels. Objective: To evaluate whether reported inappropriate antipsychotic medication use differs in severely and less severely deprived neighborhoods, and whether these differences are modified by higher levels of total nurse staffing. Design, Setting, and Participants: This was a cross-sectional analysis of a national sample of nursing homes that linked across 3 national large-scale data sets for the year 2019. Analyses were conducted between April and June 2023. Exposure: Neighborhood deprivation status (severe vs less severe) and total staffing hours (registered nurse, licensed practical nurse, certified nursing assistant). Main Outcome and Measures: This study estimated the association between neighborhood deprivation and the percentage of long-stay residents who received an antipsychotic medication inappropriately in the nursing home at least once in the past week and how this varied by nursing home staffing through generalized estimating equations. Analyses were conducted on the facility level and adjusted for state fixed effects. Results: This study included 10 966 nursing homes (1867 [17.0%] in severely deprived neighborhoods and 9099 [83.0%] in less deprived neighborhoods). Unadjusted inappropriate antipsychotic medication use was greater in nursing homes located in severely deprived neighborhoods (mean [SD], 15.9% [10.7%] of residents) than in those in less deprived neighborhoods (mean [SD], 14.2% [8.8%] of residents). In adjusted models, inappropriate antipsychotic medication use was higher in severely deprived neighborhoods vs less deprived neighborhoods (19.2% vs 17.1%; adjusted mean difference, 2.0 [95% CI, 0.35 to 3.71] percentage points) in nursing homes that fell below critical levels of staffing (less than 3 hours of nurse staffing per resident-day). Conclusions and Relevance: These findings suggest that levels of staffing modify disparities seen in inappropriate antipsychotic medication use among nursing homes located in severely deprived neighborhoods compared with nursing homes in less deprived neighborhoods. These findings may have important implications for improving staffing in more severely deprived neighborhoods.

Using Acuity to Predict Oncology Infusion Center Daily Nurse Staffing and Outcomes.

BACKGROUND: Outpatient oncology infusion centers (OICs) use acuity to quantify the complexity and intensity of care to improve staffing levels and equitable patient assignments. OIC interviews revealed inconsistent measurement of acuity and a mixture of use cases. No publications measured objective operational benefits beyond surveyed nurse satisfaction or compared different models of acuity. OBJECTIVES: This study assessed three acuity models across multiple centers to determine whether acuity was superior to patient volumes or patient hours in predicting the number of nurses needed to care for scheduled patients in an OIC, as well as the effect on objective metrics of missed nurse lunch breaks and patient wait times. A secondary end point was used to identify a superior model. METHODS: Classification machine learning models were built to assess the predictive value of three acuity models compared to patient hours and patient visits. FINDINGS: None of the tested acuity models were found to have statistically significant improvement to the prediction of needed OIC nurse staffing, patient wait times, or missed nurse lunch breaks.

Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room.

GOAL: Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy. METHODS: Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5 months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within ±30 min or ±20% of the scheduled duration if the scheduled time is ≥ or <150 min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded. PRINCIPAL FINDINGS: The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p < .001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p < .001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p = .0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p = .0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p = .22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954 hr). PRACTICAL APPLICATIONS: The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem.

With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis.

It's Time! The Path for Nursing Reimbursement Reform.

Since the COVID-19 pandemic, nurses and nurse leaders are increasingly vocal about chronic understaffing and the impact the staffing crisis continues to have on nurses' well-being and patient outcomes. The American Nurses Association's Nurse Staffing Task Force addressed the importance of staffing standards as a critically needed step toward improving patient and population health outcomes. Against the backdrop of ongoing nursing shortages, hospital leaders have been hesitant to embrace staffing ratios, expressing concerns about their ability to hire and retain sufficient nursing staff, as operational revenue margins remain thin and nursing labor is costly. This article explicates structural issues within the current nursing reimbursement model that harms hospitals' business case for investments in nurse staffing and work environments. We argue that nurses must advocate for nursing reimbursement reform to increase the nursing workforce and improve nurse staffing and work environments. Such reform is necessary to support sustained hospital investments, financial philosophies, and approaches to meaningfully address and improve nurse staffing.

Nursing Homes Increasingly Rely On Staffing Agencies For Direct Care Nursing.

When nursing homes experience a shortage in directly employed nursing staff, they may rely on temporary workers from staffing agencies to fill this gap. This article examines trends in the use of staffing agencies among nursing homes during the prepandemic and COVID-19 pandemic era (2018-22). In 2018, 23 percent of nursing homes used agency nursing staff, accounting for about 3 percent of all direct care nursing hours worked. When used, agency staff were commonly present for ninety or fewer days in a year. By 2022, almost half of all nursing homes used agency staff, accounting for 11 percent of all direct care nursing staff hours. Agency staff were increasingly used to address chronic staffing shortages, with 13.8 percent of nursing homes having agency staff present every day. Agency staff were 50-60 percent more expensive per hour than directly employed nursing staff, and nursing homes that used agency staff often had lower five-star ratings. Policy makers need to consider postpandemic changes to the nursing home workforce as part of nursing home reform, as increased reliance on agency staff may reduce the financial resources available to increase nursing staff levels and improve the quality of care.

Until Further Notice.

This essay reflects on the transition from intern to resident and a 24-hour on-call shift starting with an emergency in the pediatric intensive care unit.

Consistently Exploring Nurse Staffing and Neurocritical Care Unit Turnover.

ABSTRACT: BACKGROUND: Staffing models within nursing units have long been a hot topic of discussion. The COVID-19 pandemic exacerbated this discussion by straining the national nursing environment and workforce. Before the pandemic, the neuroscience intensive care unit (NSICU) primarily used an acuity-adjusted staffing model and aimed for a nurse-to-patient ratio of 1:1.5. During and after the pandemic, the NSICU was forced to primarily use a centralized staffing model because of the increased turnover in the hospital at large and a rise in patient census. METHODS : Unit census data in an NSICU were tracked before, during, and after the pandemic alongside utilization of a centralized staffing model in the hospital at large. RESULTS : During this time, the NSICU saw a statistically significant increase in average nurse-to-patient ratio and incidences of both floating and tripled assignments. The NSICU simultaneously saw a 180% increase in nursing turnover. CONCLUSION : Although we cannot prove that a centralized staffing model is directly responsible for higher nursing turnover, its utilization led to greater incidence of poor staffing-reflected in deviation from the nurse-to-patient ratio goal of the unit. Nurse staffing concerns play a large role in nurse satisfaction in the workforce: staffing shortages have been described both as a precursor to and as a consequence of increased nursing turnover.

Pediatric Resident Well-being: A Group Concept Mapping Study.

OBJECTIVE: Pediatric residency programs invest substantial resources in supporting resident well-being. However, no pediatric resident well-being conceptual model exists to guide interventions. This study aimed to understand how a diverse stakeholder sample conceptualized well-being. METHODS: We used group concept mapping methodology. We sent a brainstorming survey to pediatric residents and program leaders at 24 US residencies with the prompt, "The experience of well-being for resident physicians includes…" Participants at 4 residencies sorted well-being ideas conceptually and rated idea importance. We performed multidimensional scaling and hierarchical cluster analysis to develop cluster maps. Using participant feedback and a consensus-driven process, we determined best cluster representation. We used pattern matching to compare domain ratings between subgroups. RESULTS: In brainstorming, 136 residents and 22 program leaders from 22 residency programs generated 97 unique ideas. Ideas were sorted and rated by 33 residents, 14 program leaders. Eight domains aligning with 4 resident roles were identified. Domains were: 1) positive, safe, and diverse culture; 2) unity and connection; 3) professional fulfillment and mindset; 4) personal health and life satisfaction; 5) professional development and recognition; 6) schedule protections and downtime; 7) work systems and benefits; 8) proactive and compassionate leadership. Domains aligned with the following roles: 1) individual, 2) colleague, 3) employee, 4) emerging pediatrician. Residents placed higher value on schedule protections and downtime than program leaders, P < .05. CONCLUSIONS: Pediatric resident well-being may be conceptualized as inter-related domains corresponding with various resident roles. Participants aligned on many well-being priorities but differed regarding work schedules.

Beyond Hospital-Level Aggregated Data: A Methodology to Adapt Clinical Data From the Electronic Health Record for Nursing Unit-Level Research.

BACKGROUND: Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing. OBJECTIVES: Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset. RESEARCH DESIGN: We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide. We linked patient-level EHR and bar code medication administration data to nursing units using a crosswalk. We divided hospitalizations into segments based on the patient's time-stamped location (ward stays). We calculated the number of ward stays and medication administrations linked to a nursing unit and the unit-level and facility-level mean patient risk scores. RESULTS: We extracted data on 1117 nursing units, 3782 EHR patient locations associated with 1,137,391 ward stays, and 67,772 bar code medication administration locations associated with 147,686,996 medication administrations across 125 Veterans Health Administration facilities. We linked 89.46% of ward stays and 93.10% of medication administrations to a nursing unit. The average (standard deviation) unit-level patient severity across all facilities is 4.71 (1.52), versus 4.53 (0.88) at the facility level. CONCLUSIONS: Identification of units is indispensable for using EHR data to understand unit-level phenomena in nursing research and can provide the context-specific information needed by managers making frontline decisions about staffing.

Association of sleep quality with duty hours, mental health, and medical errors among Japanese postgraduate residents: a cross-sectional study.

Long duty hours (DH) impair sleep and negatively affect residents' health and medical safety. This cross-sectional study investigated the association among residents' DH, sleep duration, insomnia, sleep impairment, depressive symptoms, and self-reported medical errors among 5579 residents in Japan who completed the General Medicine In-Training Examination (2021) and participated in the training-environment survey. Weekly DH was classified under seven categories. Sleep duration and insomnia symptoms, from the Athens Insomnia Scale, were analysed to determine sleep impairment; depressive symptoms and medical errors were self-reported. Among 5095 residents, 15.5% slept < 5 h/day, and 26.7% had insomnia. In multivariable analysis, compared with ≥ 60 and < 70, DH ≥ 90 h/week associated with shorter sleep duration and worsen insomnia symptoms. Shorter durations of sleep and more intense symptoms of insomnia were associated with increased depressive symptoms. Medical errors increased only among residents with insomnia, but were not associated with sleep duration. DH > 90 h/week could lead to shorter sleep duration, worsen insomnia symptoms, and negatively impact well-being and medical safety. There was no significant association between sleep duration and medical errors; however, insomnia conferred an increased risk of medical errors. Limiting DH for residents to avoid excessive workload can help improve resident sleep, enhance resident well-being, and potentially reduce insomnia-associated medical errors.

The long-term effect of work schedule, shift work disorder, insomnia and restless legs syndrome on headache among nurses: A prospective longitudinal cohort study.

BACKGROUND: The preset study aimed to explore whether work schedules and sleep disorders predict the onset of headache. METHODS: A longitudinal study was conducted with questionnaire data from 2014 (baseline) and 2017 (follow-up) on work schedule, number of night shifts, number of quick returns, insomnia, shift work disorder (SWD), restless legs syndrome (RLS) and validated headache diagnoses among 1560 Norwegian nurses. Associations were explored by multivariate regression analyses. RESULTS: Work related factors at baseline did not predict onset of headache three years later. In the adjusted logistic regressions, insomnia at baseline predicted increased risk of new onset of migraine (odds ratio (OR) = 1.58; 95% confidence interval (CI) = 1.08-2.33), chronic headache (OR = 2.02; 95% CI = 1.04-4.66) and medication-overuse headache (OR = 3.79; 95% CI = 1.26-11.42) at follow-up. SWD at baseline predicted new onset of migraine (OR = 1.64; 95% CI = 1.07-2.50) and RLS at baseline predicted new onset of headache ≥1 day per month (OR = 1.55; 95% CI = 1.01-2.36) and migraine (OR = 1.55; 95% CI = 1.03-2.32) at follow-up. No factors predicted tension-type headache. CONCLUSIONS: Overall, work related factors did not predict the onset of headache three years later, whereas insomnia, SWD and RLS at baseline all increased the risk of future headaches.