Exploring the lived experiences of participants and facilitators of an online mindfulness program during COVID-19: a phenomenological study
Bibliographic record
Abstract
The coronavirus pandemic (COVID-19) has placed incredible demands on healthcare workers (HCWs) and adversely impacted their well-being. Throughout the pandemic, organizations have sought to implement brief and flexible mental health interventions to better support employees. Few studies have explored HCWs’ lived experiences of participating in brief, online mindfulness programming during the pandemic using qualitative methodologies. To address this gap, we conducted semi-structured interviews with HCWs and program facilitators (n = 13) who participated in an online, four-week, mindfulness-based intervention program. The goals of this study were to: (1) understand how participants experienced work during the pandemic; (2) understand how the rapid switch to online life impacted program delivery and how participants experienced the mindfulness program; and (3) describe the role of the mindfulness program in supporting participants’ mental health and well-being. We utilized interpretive phenomenological analysis (IPA) to elucidate participants’ and facilitators’ rich and meaningful lived experiences and identified patterns of experiences through a cross-case analysis. This resulted in four main themes: (1) changing environments; (2) snowball of emotions; (3) connection and disconnection; and (4) striving for resilience. Findings from this study highlight strategies for organizations to create and support wellness programs for HCWs in times of public health crises. These include improving social connection in virtual care settings, providing professional development and technology training for HCWs to adapt to rapid environmental changes, and recognizing the difference between emotions and emotional states in HCWs involved in mindfulness-based programs.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".