Role of an Online Skill-Based Mindfulness Program for Healthcare Worker's Resiliency During the COVID-19 Pandemic: A Mixed-Method Study
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the mental health care needs of health care workers. The primary aim of this study was to explore the effects of an online mindfulness program on resiliency in health care workers during the pandemic using a mixed-methods approach. An online 4-week mindfulness program was delivered to healthcare workers ( N = 130) in Ontario, Canada. Resiliency was assessed at 3-time points (i.e., pre, post, and 1-month follow-up), and the mindfulness program's efficacy on resiliency was analyzed using linear regression. Semi-structured interviews ( N = 10) were conducted to substantiate their experiences. Healthcare workers' resiliency significantly increased after the mindfulness program compared to the baseline, maintaining the effect after 1 month in both unadjusted and adjusted regression models. These findings were further bolstered by the positive experiences shared by participants highlighting the program's efficacy on empathy and resiliency. Evidence suggests that mindfulness is associated with promoting resiliency in healthcare workers and may be an important strategy to promote resiliency in this population.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".