The long‐term effects of psychological demands on chronic fatigue
Bibliographic record
Abstract
AIM: Investigate the impact of psychological job demands and resources on chronic fatigue. BACKGROUND: Nurse fatigue is a serious problem with negative consequences on patient safety and nurse well-being. Excessive job demands can exacerbate nurse fatigue, which may limit the ability of nurses to engage in professional practice. METHODS: This two-wave study was carried out with a self-report questionnaire administered to nurses in eastern Canada (n = 154). Cross-lagged analysis using structural equation modelling was conducted to examine the interactions between psychological job demands, resources and chronic fatigue over time. RESULTS: Results showed that psychological job demands predicted chronic fatigue a year later. Nonetheless, job resources (decision latitude, social support) did not buffer the relationship between psychological job demands and chronic fatigue 1 year later. CONCLUSIONS: Psychological demands have a long-term effect on chronic fatigue, thus interventions to mitigate fatigue are needed. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should be aware of the cumulative effects of chronic fatigue and implement strategies to create a better balance between job demands and resources in the workplace.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".