Building resilience for healthcare professionals working in an Italian red zone during the COVID‐19 outbreak: A pilot study
Bibliographic record
Abstract
The COVID-19 pandemic has placed considerable strain on healthcare workers showing high rates of stress and psychological health problems. Interventions are urgently needed to help healthcare workers perform under conditions of great risk and uncertainty. In particular, healthcare leadership is known to be critical to supporting healthcare workers to deal with an uncertain and distressing healthcare environment. This pilot study evaluated the impact of the R2 resilience program tailored for healthcare leaders working in a highly affected COVID-19 area in Italy. Through two group cohorts, 21 healthcare leaders completed the intervention, with 17 participants providing pre- and post-intervention assessment data. Sixty-two staff members who benefitted from their coordinators' resilience-focused leadership were also included in the study. Findings show that participation in R2 was associated with reduction in levels of perceived stress and burnout symptoms, and increases in rugged qualities, self-efficacy and in social-ecological resilience. Significant changes in rugged qualities, self-efficacy and perceived stress were also detected in staff members. High rates of participants' program satisfaction have been detected. R2 is a promising intervention for healthcare professionals working in emergency settings designed to enhance the rugged qualities and resources required to deal with heightened exposure to stress.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".