A re‐examination of the individual differences approach that explains occupational resilience and psychological adjustment among nurses
Bibliographic record
Abstract
AIMS: This study re-examines the validity of a model of occupational resilience for use by nursing managers, which focused on an individual differences approach that explained buffering factors against negative outcomes such as burnout for nurses. BACKGROUND: The International Collaboration of Workforce Resilience model (Rees et al., 2015, Frontiers in Psychology, 6, 73) provided initial evidence of its value as a parsimonious model of resilience, and resilience antecedents and outcomes (e.g., burnout). Whether this model's adequacy was largely sample dependent, or a valid explanation of occupational resilience, has been subsequently un-examined in the literature to date. To address this question, we re-examined the model with a larger and an entirely new sample of student nurses. METHODS: = 26.4 (7.7) years), with data examined via a rigorous latent factor structural equation model. RESULTS: The model upheld many of its relationship predictions following further testing. CONCLUSIONS: The model was able to explain the individual differences, antecedents, and burnout-related outcomes, of resilience within a nursing context. IMPLICATIONS FOR NURSING MANAGEMENT: The results highlight the importance of skills training to develop mindfulness and self-efficacy among nurses as a means of fostering resilience and positive psychological adjustment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".