P-584 UNDERSTANDING BURNOUT AMONG CANADIAN MEDICAL LABORATORY PROFESSIONALS WORKING DURING THE SECOND WAVE OF THE COVID-19 PANDEMIC IN ONTARIO, CANADA: USING A GENDERED-BASED ANALYSIS
Bibliographic record
Abstract
Abstract Introduction Medical laboratory professionals are mostly women and play a crucial role in delivering health services and provide and perform millions of tests daily on blood, body fluids, cells and tissues. The study examined the mental health and well-being of medical laboratory professionals working during the second wave of the COVID-19 pandemic. Methods A sequential explanatory mixed-methods study was conducted to explore mental health outcomes in medical laboratory professionals, including medical laboratory technologists, medical laboratory assistants, and medical laboratory technicians, working in Ontario, Canada. A self-reported questionnaire on burnout and job stress was administered, and Two focus groups were also conducted. Thematic analysis was used to develop themes and subthemes. Results A total of 441 (47.5% response rate) medical laboratory professionals completed the survey. Most of the respondents self-identified as female (90.2%). Most of the medical laboratory professionals were women, with a mean age of 43.1 and a standard deviation of 11.7. The prevalence of burnout was 72.3% for medical laboratory technologists. In the adjusted demographic model, those ≥50 (OR = 0.36, 95% CI: 0.22–0.59) were approximately one-third as likely to experience burnout as those under 50. The qualitative focus groups demonstrated four key themes: staff shortage, feeling forgotten, work environment, and resilience. Discussion There is limited research regarding the workplace mental health of medical laboratory professionals. This study provides preliminary evidence regarding their mental health and well-being in the workplace. Conclusion Future research is warranted to understand the relationship between the workplace mental health of these workers and its impact on their mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".