Resilience Training for Hospital Workers in Anticipation of an Influenza Pandemic
Bibliographic record
Abstract
BACKGROUND: Well before the H1N1 influenza, health care organizations worldwide prepared for a pandemic of unpredictable impact. Planners anticipated the possibility of a pandemic involving high mortality, high health care demands, rates of absenteeism rising up to 20-30% among health care workers, rationing of health care, and extraordinary psychological stress. METHOD: The intervention we describe emerged from the recognition that an expected influenza pandemic indicated a need to build resilience to maintain the health of individuals within the organization and to protect the capacity of the organization to respond to extraordinary demands. Training sessions were one component of a multifaceted approach to reducing stress through effective preparation and served as an evidence based platform for our hospital's response to the H1N1 pandemic. RESULTS: The training was delivered to more than 1250 hospital staff representing more than 22 departments within the hospital. The proportion of participants who felt better able to cope after the session (76%) was significantly higher than the proportion who felt prepared to deal confidently with the pandemic before the session (35%). Ten key themes emerged from our qualitative analysis of written comments, including family-work balance, antiviral prophylaxis, and mistrust or fear towards health care workers. CONCLUSIONS: Drawing on what we learned from the impact of SARS on our hospital, we had the opportunity to improve our organization's preparedness for the pandemic. Our results suggest that an evidence-based approach to interventions that target known mediators of distress and meet standards of continuing professional development is not only possible and relevant, but readily supportable by senior hospital administration.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".