De-essentializing racial pain: Stories of Filipino health care workers
Bibliographic record
Abstract
This article focuses on findings of a qualitative research study that looked at experiences of Filipino healthcare workers in Canada during the COVID-19 pandemic. The purpose is to contribute to the growing body of literature on mental health among racialized frontline healthcare workers in Canada by investigating factors that affect mental health and barriers associated with accessing services and supports among Filipino healthcare workers in Ontario, Canada. The study employed a cross-sectional qualitative descriptive design to identify strategies that Filipino frontline healthcare workers use to effectively cope with mental health issues, work stress, and structural and economic barriers to their well-being. The study conducted in-depth semi-structured and open-ended interviews with 15 female Filipino healthcare workers. Findings indicate that social support received from colleagues, managers, families, and friends, through forms of assistance and protection, are crucial for dealing with various mental health stressors in the workplace during healthcare crises. Participants indicated that adequate social support help frontline healthcare professionals effectively manage stressful events, including the COVID-19 pandemic.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".