Understanding individual resilience in the workplace: the international collaboration of workforce resilience model
Bibliographic record
Abstract
When not managed effectively, high levels of workplace stress can lead to several negative personal and performance outcomes. Some professional groups work in highly stressful settings and are therefore particularly at risk of conditions such as anxiety, depression, secondary traumatic stress, and burnout. However, some individuals are less affected by workplace stress and the associated negative outcomes. Such individuals have been described as "resilient." A number of studies have found relationships between levels of individual resilience and specific negative outcomes such as burnout and compassion fatigue. However, because psychological resilience is a multi-dimensional construct it is necessary to more clearly delineate it from other related and overlapping constructs. The creation of a testable theoretical model of individual workforce resilience, which includes both stable traits (e.g., neuroticism) as well as more malleable intrapersonal factors (e.g., coping style), enables information to be derived that can eventually inform interventions aimed at enhancing individual resilience in the workplace. The purpose of this paper is to introduce a new theoretical model of individual workforce resilience that includes several intrapersonal constructs known to be central in the appraisal of and response to stressors and that also overlap with the construct of psychological resilience. We propose a model in which psychological resilience is hypothesized to mediate the relationship between neuroticism, mindfulness, self-efficacy, coping, and psychological adjustment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".