Relationship between three aspects of resilience—adaptive characteristics, withstanding stress, and bouncing back—in hospital workers exposed to prolonged occupational stress during the COVID-19 pandemic: a longitudinal study
Bibliographic record
Abstract
BACKGROUND: The term resilience is used to refer to multiple related phenomena, including: (i) characteristics that promote adaptation to stressful circumstances, (ii) withstanding stress, and (iii) bouncing back quickly. There is little evidence to understand how these components of resilience are related to one another. Skills-based adaptive characteristics that can respond to training (as opposed to personality traits) have been proposed to include living authentically, finding work that aligns with purpose and values, maintaining perspective in the face of adversity, managing stress, interacting cooperatively, staying healthy, and building supportive networks. While these characteristics can be measured at a single time-point, observing responses to stress (withstanding and bouncing back) require multiple, longitudinal observations. This study's aim is to determine the relationship between these three aspects of resilience in hospital workers during the prolonged, severe stress of the COVID-19 pandemic. METHODS: We conducted a longitudinal survey of a cohort of 538 hospital workers at seven time-points between the fall of 2020 and the spring of 2022. The survey included a baseline measurement of skills-based adaptive characteristics and repeated measures of adverse outcomes (burnout, psychological distress, and posttraumatic symptoms). Mixed effects linear regression assessed the relationship between baseline adaptive characteristics and the subsequent course of adverse outcomes. RESULTS: The results showed significant main effects of adaptive characteristics and of time on each adverse outcome (all p < .001). The size of the effect of adaptive characteristics on outcomes was clinically significant. There was no significant relationship between adaptive characteristics and the rate of change of adverse outcomes over time (i.e., no contribution of these characteristics to bouncing back). CONCLUSIONS: We conclude that training aimed at improving adaptive skills may help individuals to withstand prolonged, extreme occupational stress. However, the speed of recovery from the effects of stress depends on other factors, which may be organizational or environmental.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".