COVID-19 Mental Health Stressors of Health Care Providers in the Pandemic Acceptance and Commitment to Empowerment Response (PACER) Intervention: Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Since the pandemic, more Canadians have reported poorer mental health. A vital group experiencing a high level of stressors consists of health care providers (HCPs) caring for COVID-19 patients, carrying out public health responses, or working with vulnerable populations. The mental health of HCPs is negatively affected by the pandemic, not only at work but also at home and in the community. Intersecting stressors at multiple levels contribute to HCPs’ experiences of fatigue, insomnia, anxiety, depression, and posttraumatic stress symptoms. OBJECTIVE The aim of this qualitative study was to explore the pandemic stressors experienced by HCPs at work, at home, and in the community before participating in the Pandemic Acceptance and Commitment to Empowerment Response (PACER) online intervention. METHODS Informed by a social ecological approach, we used a qualitative reflective approach to engage 74 HCPs in diverse roles. Data were collected during the first 2 waves of the COVID-19 pandemic (June 2020 to February 2021) in Canada. RESULTS Informed by a social ecological framework, 5 overarching themes were identified in our thematic analysis: (1) personal level stressors that highlight HCPs’ identities and responsibilities beyond the workplace; (2) interpersonal level stressors from disrupted social relationships; (3) organizational stressors that contributed to unsettled workplaces and moral distress; (4) community and societal stressors attributed to vicarious trauma and emotional labor; and (5) the multilevel and cumulative impacts of COVID-19 stressors on HCPs’ health. CONCLUSIONS COVID-19 is not merely a communicable disease but also a social and political phenomenon that intensifies the effects of social inequities. Current understanding of pandemic stressors affecting HCPs is largely partial in nature. Although workplace stressors of HCPs are real and intense, they need to be explored and understood in the context of stressors that exist in other domains of HCPs’ lives such as family and community to ensure these experiences are not being silenced by the “hero” discourses or overshadowed by professional demands.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".