Factors Associated With the Psychological Impact of Severe Acute Respiratory Syndrome on Nurses and Other Hospital Workers in Toronto
Bibliographic record
Abstract
OBJECTIVES: A survey was conducted to measure psychological stress in hospital workers and measure factors that may have mediated acute traumatic responses. METHODS: A self-report survey was completed by 1557 healthcare workers at three Toronto hospitals in May and June 2003. Psychological stress was measured with the Impact of Event Scale. Scales representing attitudes to the outbreak were derived by factor analysis of 76 items probing attitudes to severe acute respiratory syndrome. The association of Impact of Event Scale scores to job role and contact with severe acute respiratory syndrome patients was tested by analysis of variance. Between-group differences in attitudinal scales were tested by multivariate analysis of variance. Attitudinal scales were tested as factors mediating the association of severe acute respiratory syndrome patient contact and job role with total Impact of Event Scale by linear regression. RESULTS: Higher Impact of Event Scale scores are found in nurses and healthcare workers having contact with patients with severe acute respiratory syndrome. The relationship of these groups to the Impact of Event Scale score is mediated by three factors: health fear, social isolation, and job stress. CONCLUSIONS: Although distress in response to the severe acute respiratory syndrome outbreak is greater in nurses and those who care for patients with severe acute respiratory syndrome, these relationships are explained by mediating variables that may be available for interventions to reduce stress in future outbreaks. In particular, the data suggest that the targets of intervention should include job stress, social isolation, and health fear.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".