SARS: coping with the impact at a community hospital
Bibliographic record
Abstract
AIM: This paper presents the findings of a staff survey conducted at a 350-bed acute care facility located on the periphery of Toronto, Canada. BACKGROUND: Toronto's severe acute respiratory syndrome (SARS) crisis resulted in trauma-like effects at hospitals hardest hit by the disease. A systematic examination of the impact on staff working in hospitals that saw relatively few cases, while maintaining the precautions associated with elevated alert levels, has not been undertaken. METHODS: A questionnaire was distributed for 1 month commencing 17 April 2003 and 300 completed responses were obtained (approximately one in six staff members). The data collected included demographic and occupational information, in addition to perceptions of SARS' impact on patient care, factors contributing to adverse impacts on patient care, working conditions, decision-making, communication and relations, sources of support, and the impact on workers' lives outside work. Items for these sections were developed by a multi-disciplinary team of health care workers and hospital administrators. RESULTS: In the absence of pre-SARS normative data for the survey, demographic and occupational variables were used to look for patterns of differences between relevant subgroups of respondents. Statistically significant differences were found for gender (73.9% women), nurses (24.7%) vs. others, doctors (20.3%) vs. others, older (40 years or older, 60.0%) vs. younger persons, emergency or intensive care unit workers (8.0%) vs. others, and those employed fewer years at the hospital (less than 5 years: 46.2%) vs. five or more years. These differences varied across the following domains: factors adversely affecting patients, communications, support, working conditions, decision-making and, to a lesser extent, impact on life outside work. While all groups found SARS stressful, nurses reported a greater impact on morale and job satisfaction. Nurses relied more on peer support than doctors, felt less informed and less involved in decision-making than doctors felt, and were more likely to report that infection control procedures were not strict enough. CONCLUSIONS: The between-group differences and the pattern of these differences clearly illustrate the polarizing and stressful impact SARS had at a hospital with only a small number of probable or suspect cases. The clear differences between groups defined by demographics, professions and clinical roles suggest a subtle and pervasive secondary impact of the SARS outbreak, with repercussions health care facilities must contend with while maintaining increased levels of vigilance in the wake of SARS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".