The survey was conducted from April 15 to October 15, 2025, and achieved a response rate of 63.1%. It excluded nurses employed in intensive care units, operating rooms and emergency departments. Generation Z represented 50.7% of participants; 90.0% were women, and 58.8% worked in medical, or non-surgical, wards.

The primary comparison grouped Generation Z separately from nurses belonging to Generation Y and Generation X/Baby Boomers. Participants completed a study-specific sociodemographic and occupational questionnaire, the Mini-COPE inventory and the General Self-Efficacy Scale. Across the full sample, active coping had the highest mean Mini-COPE score at 2.13, followed by planning at 2.09, while substance use had the lowest score at 0.31.

The mean general self-efficacy score was 29.14, with a standard deviation of 4.66. The primary multivariable analysis accounted for professional experience, weekly working hours, additional employment, shift-work status, the number of patients hospitalized per shift, the number of patients per nurse and the type of workplace. In this model, the combined older group had general self-efficacy scores 1.38 points lower than Generation Z, with a 95% confidence interval of −2.47 to −0.29 and a reported q value of 0.0250.

Separate sensitivity analyses comparing Generation Z with Generation Y and with Generation X/Baby Boomers showed similar differences. The adjusted self-efficacy result differed from the initial unadjusted comparison. The groups also differed in several occupational characteristics: 55.7% of the older group reported additional employment, compared with 18.4% of Generation Z participants, and the older group worked an average of 49.48 hours per week versus 40.26 hours for Generation Z.

The abstract reports that Generation Z nurses differed from the older group in the coping strategies they used and that several differences remained significant after adjustment for the measured sociodemographic and occupational factors. That suggests the observed differences were not fully explained by the variables included in the analysis. However, the supplied findings do not give every generation-specific Mini-COPE estimate, so the size and direction of individual strategy comparisons cannot be specified from the reported results.

Higher general self-efficacy was associated with several coping strategies in the overall sample, but this association does not establish that self-efficacy caused any particular response to stress or that one coping strategy produced a better clinical outcome. The investigators did not test an intervention, and the survey does not demonstrate reduced burnout, improved quality of care or greater team stability. Nor can the cross-sectional design determine whether coping patterns and self-efficacy changed over time or separate generation from age, historical period, career stage and workload.

The authors identified mentoring, workload monitoring, support for recovery and protection of work–life balance as potential management strategies for nursing workforces. They emphasized that the effectiveness of these approaches requires evaluation in prospective studies. As a survey conducted at one Polish hospital and excluding three high-intensity clinical areas, the research offers a limited but detailed snapshot of professional experience among 760 nurses; its adjusted findings identify patterns that merit further study rather than proof of an inherent generational difference.

Subject of Research: Generational differences in coping strategies and self-efficacy among hospital nurses

Article Title: Does generation Z cope with stress differently? Self-efficacy, coping strategies, and implications for nursing workforce management: a cross-sectional survey study

Article References: Paplaczyk-Serednicka, M., Staszkiewicz, M., Wierzbiak, P., Musz, E., Orzeł-Nowak, A., Kuźmicz, I., Repka, I., Pluta, J., & Ścisło, L. (2026). Does generation Z cope with stress differently? Self-efficacy, coping strategies, and implications for nursing workforce management: a cross-sectional survey study. BMC Nursing. https://doi.org/10.1186/s12912-026-05567-y

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05567-y

Keywords: Generation Z, nursing, occupational stress, self-efficacy, coping strategies, workload, cross-sectional survey, hospital workforce, Does, generation, cope, stress

The research team recruited 111 nurses working at a university hospital in Türkiye, a sample drawn from a workforce that is simultaneously among the most demanding in healthcare and among the most digitally connected. Ethical approval was obtained from the Ethics Committee of Biruni University under decision number 2023/85-60, and the study was conducted in accordance with institutional and national ethical guidelines as well as the 1964 Declaration of Helsinki and its later amendments. Every participant provided informed consent before data collection began, and the authors declared no competing interests and no external funding for the work.

Methodologically, the study rested on two well-established psychometric instruments. The first was the Nomophobia Questionnaire, or NMP-Q, a 20-item scale that yields scores reflecting the severity of nomophobic symptoms across dimensions such as fear of losing communication, loss of connectedness, lack of access to information, and giving up convenience. The second was the Individual Work Performance Questionnaire, the IWPQ, which measures perceived job performance across task performance, contextual performance, and counterproductive behavior. Participants also completed a nurse information form capturing demographic details and patterns of smartphone use, including daily screen time, phone use during working hours, and whether they personally considered their smartphone use excessive.

The headline numbers from the survey are striking in their own right. The mean NMP-Q score among the 111 nurses was 73.37 with a standard deviation of 27.25, indicating a substantial degree of nomophobic tendency in this population. The mean IWPQ total score was 3.86 with a standard deviation of 0.69, a figure that sits toward the favorable end of the performance scale. In other words, these were nurses who reported meaningful smartphone dependence but who nonetheless rated their own work functioning as generally solid—a discrepancy that immediately hints at the complexity the statistical analysis would go on to reveal.

The primary analysis examined the Pearson correlation between total NMP-Q and IWPQ scores, reported with a 95 percent confidence interval. The result was a correlation coefficient of −0.098, with a confidence interval spanning −0.279 to 0.090 and a p-value of 0.305. Because the interval crosses zero, the study could not confirm a statistically significant association between nomophobia and perceived job performance. A supplementary Spearman rank correlation, which is robust to outliers and non-normal distributions, produced a nearly identical estimate of −0.105 with a p-value of 0.274, reinforcing the same conclusion. The negative sign of both coefficients is directionally consistent with the hypothesis that higher nomophobia accompanies lower performance, but the magnitude is small and the uncertainty around it is wide.

The authors were careful to characterize what this null result actually means, and their framing is a lesson in statistical humility. The estimated association, they write, is compatible with both no association at all and a small-to-moderate negative association. With 111 participants, the study simply lacks the statistical power to distinguish between those possibilities. This is a common and underappreciated feature of correlation studies: a wide confidence interval does not prove that no relationship exists, it proves only that the data cannot yet pin one down. For hospital administrators tempted to conclude that smartphone anxiety is irrelevant to nursing performance, the honest reading is that the question remains open, pending larger samples.

Where the study did find robust signals was in its exploratory analyses of smartphone behavior, and here the statistical rigor is notable. The researchers applied false-discovery-rate, or FDR, correction to their secondary comparisons—a procedure that adjusts p-values to limit the number of false positives that inevitably arise when many tests are run simultaneously. After this correction, NMP-Q scores differed significantly according to daily smartphone use (q = 0.003), smartphone use during work (q = 0.011), and perceived excessive smartphone use (q = 0.003). Intriguingly, none of the comparisons involving IWPQ total scores survived the FDR correction, meaning that smartphone habits predicted nomophobia levels but not, in this dataset, perceived performance.

The adjusted analysis drove the point home with a multivariable linear regression using heteroskedasticity-consistent HC3 standard errors, a technique that guards against distorted confidence intervals when the variability of outcomes differs across groups. In this model, nurses who reported more than seven hours of daily smartphone use had NMP-Q scores 21.36 points higher than those reporting one to three hours per day, with a 95 percent confidence interval of 5.52 to 37.20 and a p-value of 0.008. That is a substantial effect—roughly three-quarters of a standard deviation of the nomophobia scale—suggesting that heavy daily screen time and nomophobic symptoms travel together in this population. The causal arrow, however, cannot be determined from cross-sectional data: it is equally plausible that anxious, phone-dependent individuals gravitate toward longer use, or that prolonged use itself cultivates dependence.

Why does the disconnect between nomophobia and performance matter? One plausible interpretation is that modern clinical environments have absorbed the smartphone into the workflow itself. Nurses routinely use phones for shift coordination, drug references, and institutional messaging, so device access may no longer function purely as a distraction. Alternatively, the IWPQ captures perceived performance, and self-assessments may be insensitive to subtle attentional lapses that nomophobia could produce—objective measures such as medication error rates or task-completion times might reveal effects that self-reports miss. The study’s authors emphasize that their findings should be interpreted cautiously given the sample size and the exploratory nature of the secondary analyses, and this restraint is warranted: a single-site study of 111 nurses cannot speak for the profession globally.

Nevertheless, the research adds a valuable data point to a growing literature on digital behavior in high-stakes workplaces. Its most actionable finding may be the strong link between daily smartphone duration and nomophobia scores, which identifies heavy users as a group worth monitoring—not because their performance is demonstrably suffering, but because the psychological burden of phone dependence is real and the long-term consequences in safety-critical settings remain unresolved. As hospitals worldwide grapple with mobile device policies, this study suggests the conversation should be grounded in measured evidence rather than reflexive alarm: the fear of being phoneless is prevalent among nurses, but its impact on the quality of care they deliver is, for now, statistically unproven and scientifically undecided.

Subject of Research: The relationship between nomophobia and job performance among hospital nurses

Article Title: Investigation of the relationship between nomophobia and job performance among nurses

Article References: Ersoy, F., & Karaca, A. (2026). Investigation of the relationship between nomophobia and job performance among nurses. BMC Nursing. https://doi.org/10.1186/s12912-026-05445-7

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05445-7

Keywords: nomophobia, nurses, job performance, smartphone use, BMC Nursing, cross-sectional study, NMP-Q, IWPQ, occupational health psychology, nursing research, statistical correlation, hospital workforce

The study, published in BMC Nursing by Elizabeth A. M. Michels of the Else Kröner Fresenius Center for Digital Health at TUD Dresden University of Technology and colleagues, took an observational, multimethod approach to a problem that has long frustrated occupational health researchers. Surveys about job demands and resources can tell us how nurses feel about their work in general, but they blur the very moments that accumulate into exhaustion, sickness absence, and ultimately the workforce shortages that hospitals around the world are struggling to contain. What the Dresden team wanted to know was simpler and sharper: which specific events during a normal shift actually correspond to an acute stress response in the body?

To answer that question, the researchers equipped up to 85 nurses with wearable electrocardiography devices that recorded heart rate variability continuously across an eight-hour shift. Heart rate variability, and in particular a measure called the root mean square of successive differences between heartbeats, or RMSSD, is a well-established window into the autonomic nervous system. When the sympathetic branch of that system ramps up in response to a stressor, the natural beat-to-beat variation in heart rhythm tends to decline. The researchers used the natural logarithm of this measure, lnRMSSD, as their physiological outcome, allowing them to detect moment-to-moment shifts in autonomic state as the shift unfolded.

Physiology alone, however, cannot say what caused a dip in heart rate variability. That is where the second layer of the design came in. Trained observers shadowed the nurses throughout their shifts, continuously logging the work events that occurred and rating the nurses’ momentary stress on a visual analogue scale. This ecological momentary assessment strategy, pairing event-level observation with ambulatory physiological monitoring, meant that every logged event could be linked to the cardiac state recorded at the same time. The researchers also controlled for physical activity, measured in metabolic equivalents of task, which is a crucial statistical adjustment in a job that involves a great deal of walking, lifting, and rushing; without it, a racing heart from climbing stairs could easily be mistaken for a racing heart from stress.

The events were sorted into three broad categories: clinical care burden, covering direct patient care demands; organizational burden, covering administrative and systemic frictions; and positive events, the moments of satisfaction, connection, and success that punctuate clinical work. In total, the analysis drew on 1,617 event-level observations, a dataset rich enough to support multilevel models that properly account for the fact that many events nest within each individual nurse. The findings were strikingly consistent for the observer-rated stress measure. Clinical care burden was associated with higher momentary stress than the other event categories, while positive events were associated with the lowest stress of all.

The physiological picture added an important nuance. Clinical care burden was linked to lower lnRMSSD than organizational burden, indicating a stronger autonomic stress signature when nurses were absorbed in demanding patient care than when they were wrestling with paperwork and system obstacles. Interestingly, no significant differences emerged between positive events and the other categories on the physiological side, suggesting that the recovery signal of a good moment may not register as clearly in cardiac data as the strain of a difficult one registers in an observer’s judgment. Stress, in other words, is not a single thing: what an observer sees and what the heart does can diverge in informative ways.

The study also probed whether individual characteristics shaped these patterns. Chronic stress, measured before the shifts, was associated with higher observer-rated stress during work, consistent with the idea that baseline strain colors how demanding each moment feels. An exploratory association appeared between sex and heart rate variability, with male nurses showing higher lnRMSSD values on average, a finding the authors flag as exploratory and one that echoes known sex differences in cardiac autonomic control. Age, by contrast, did not emerge as a significant factor in these analyses. These person-level results remind us that event effects unfold against the backdrop of who the nurse is and what they carry into the ward.

One of the most practically interesting parts of the study concerned the patient call system, the ubiquitous network of buttons and buzzers through which hospital patients summon help. The researchers examined whether call events differed from non-call work events, and found no significant differences in either observer-rated stress or heart rate variability. But when they dug into the reasons behind the calls, a pattern appeared. Calls related to comfort and service issues were rated as less stressful than calls involving acute medical needs or calls requiring care and mobilization support. The alarm itself, it seems, is not the stressor; what lies behind the alarm is what matters. No significant HRV associations were found for call reasons, keeping this a story about perceived demands rather than cardiac responses.

Methodologically, the study is a proof of concept as much as a set of findings. The authors report that combining event-based assessment with ambulatory physiological monitoring is feasible in routine clinical practice, which is no small claim in an environment where every extra device and every extra observer competes with patient care for attention and time. The multilevel modeling framework, using restricted maximum likelihood estimation and centering within clusters to separate within-person event effects from between-person differences, represents the current standard for this kind of intensive longitudinal data. The result is a method with high ecological validity: it captures stress where and when it actually happens, rather than asking nurses to reconstruct it retrospectively at the end of a draining shift.

Why does this matter beyond the wards of one Dresden hospital? Because targeted interventions need targets. If clinical care burden, rather than organizational friction, is the most consistently stress-associated category of everyday work, then efforts to protect nurses’ health, whether through staffing ratios, task redesign, teamwork support, or recovery breaks timed to the rhythm of patient care, can be aimed at the moments that physiology confirms are the hardest. With stressful working conditions contributing substantially to nurses’ sickness absence and to the global nursing shortage, precision about which events drive acute stress is not an academic luxury. It is the difference between broad, expensive wellness programs and focused changes to the actual texture of the working day. The Dresden team’s work, supported by the German Federal Ministry of Research, Technology and Space as part of the SEMECO project, suggests that the technology to see that texture clearly, a small wearable and a careful observer, is already within reach of routine practice.

Subject of Research: Psychophysiological measurement of acute stress responses to everyday work events in hospital nursing

Article Title: Identifying stress-relevant events in everyday nursing practice using psychophysiological outcomes – an observational multimethod study

Article References: Michels, E. A. M., Rominger, C., Schwerdtfeger, A. R., Kurze, G. F., Klein, J., Steinhäuser-Meerz, J. L., Tolle, H., & Wekenborg, M. K. (2026). Identifying stress-relevant events in everyday nursing practice using psychophysiological outcomes – an observational multimethod study. BMC Nursing, 25(1), Article 899. https://doi.org/10.1186/s12912-026-05434-w

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05434-w

Keywords: nursing, occupational stress, heart rate variability, ecological momentary assessment, wearable sensors, multilevel modeling, clinical work events, hospital workforce, autonomic nervous system, job demands-resources, Identifying, stress-relevant