Implementing the STEADY Wellness Program to Support Healthcare Workers throughout the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has posed an ongoing threat to the mental wellbeing of countless individuals worldwide, with healthcare workers at particularly high risk. We developed the STEADY staff wellness program prior to the pandemic based on the available literature and input from stakeholders, guided by the Knowledge-to-Action (KTA) Implementation Science Framework. We quickly adapted the STEADY program for implementation in selected high-need units within Canada's largest trauma hospital during the pandemic's first wave. This brief report describes implementation of the STEADY program, retroactively applying the structure of the Knowledge-to-Action Implementation Science Framework to the practical steps taken. We identified the importance of more frequent, shorter contact with HCWs that occurred in-person, with an emphasis on peer support. A flexible approach with strong support from hospital leadership were key facilitators. Our findings suggest that a flexible approach to practical program implementation, theoretically underpinned in best-practices, can result in an acceptable program that promotes increased HCW wellbeing during a pandemic.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".