Women healthcare workers, compassion fatigue, and the COVID-19 pandemic
Bibliographic record
Abstract
Abstract This mixed-methods study focuses on women essential healthcare workers and their experiences of compassion fatigue during the COVID-19 pandemic. The pandemic exacerbated compassion fatigue levels in healthcare, a field predominantly comprised of women. The study used quantitative (survey) and qualitative methods (interviews). For the survey, I received 44 viable responses. These questions sought feedback on each participant’s experience with compassion fatigue, alterations to the services that they offered, and experiences with ever-changing regulations and restrictions. They also highlighted the unique struggles that participants potentially faced as caregivers and how they were able to balance the ever-changing demands. Survey participants were invited to participate in a follow-up interview. The interview questions focused on individual impacts and experiences in their role and their personal life exploring experiences of moral injury, guilt, and shame. Participants also reported on what support they had, and recommendations they had for the government, their employer, their managers, and the public. Eight women across Ontario completed the interview, which began with the Professional Quality of Life Scale (ProQOL). The six themes from the survey and interviews were, theme 1: The Impacts of the Pandemic on Participants’ Personal Lives including the Emotional and Mental Health Impacts of Compassion Fatigue, The Physical Impacts of Compassion Fatigue and The Impacts on Family, Theme 2: Participants Experiences with Moral Injury, Theme 3: Participant Feelings of Guilt and Shame, Theme 4: Supports Provided and Sought for their Well-Being, and Theme 5: Perceived Gaps and Limitations including The Experiences with the Federal and Provincial Government, Experiences with their Healthcare Employer and Experiences with the Public, and lastly, Theme 6: Recommendations for Improving Systems and Supports. The goal was to share information about women’s firsthand experiences with Compassion Fatigue. Although differences existed among participants, they shared the common perspective that the last few years were challenging personally and professionally. This study’s mixed methods design enabled this researcher to explore women healthcare workers’ direct firsthand experiences. This study contributes novel evidence that highlights women’s challenges and their recommendations for changes needed and future improvements in support provided to them by the healthcare system and workplaces.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 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".