Feasibility of Implementing the STEADY Wellness Program to Support Hospital Staff During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: The COVID-19 Pandemic negatively impacted the mental wellbeing of healthcare workers worldwide. Many organizations responded reactively to their staff needs. The novel, evidence-informed Social Support, Tracking Distress, Education and Discussion Community (STEADY) program was implemented, with senior leadership support across a large hospital. STEADY is a multi-pronged program developed to mitigate occupational stress injury in healthcare workers and first responders. This project examined the feasibility of implementing STEADY across hospital units during a pandemic. Method: STEADY was implemented in five acute care units and across the rehab site of a large hospital. Data was collected on the five program components (drop-in peer support groups and critical incident debriefs, psychoeducation workshops, wellness assessments, peer partnering, community-building initiatives). Most peer support groups were facilitated by the program manager trained in peer support and one of six clinical staff. Results: The program was iteratively adapted to meet the needs of target units/groups. More than 300 sessions were run in ~one year, for an average of ~1.15 sessions per unit per week. With flexible adaptation to the mode of facilitation, ~75% of planned workshops and ~85% of peer support sessions were run. Three critical incident stress debriefs were held. The formal partnering program was offered via e-mail with minimal uptake. Ninety-five wellness assessments were completed by target end-users, with 36 personalized responses sent. Gratitude trees were posted in each unit for community-building. Eight target unit staff completed formal peer support facilitation training. Twenty additional groups across the organization requested STEADY programming support and ten requested gratitude trees. Conclusion: Results indicate that most components of the STEADY program were feasible to implement in hospital units during the pandemic. On-site, interactive programming was most engaging for end-users. Leadership support and flexible, continuous adaption by program leaders were identified as facilitators to program implementation and uptake.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".