Factors associated with burnout among medical laboratory professionals in Ontario, Canada: An exploratory study during the second wave of the COVID‐19 pandemic
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine factors associated with burnout among medical laboratory technologists (MLT) in Ontario, Canada during the second wave of coronavirus disease 2019 pandemic. METHODS: We employed a cross-sectional design and used a self-reported questionnaire designed for MLT in Ontario, Canada. RESULTS: There were 441 (47.5% response rate) MLT who were included in the analytic sample. Most of the respondents were women, with a mean age of 43.1 and a standard deviation of 11.7. The prevalence of experiencing burnout was 72.3% for MLT. In the adjusted demographic model, those ≥50 (OR = 0.36, 95% CI: 0.22-0.59) were 0.36 or about one third as likely to experience burnout as those under 50. Similarly, those who held a university degree were less likely to experience burnout compared with high school degree (OR = 0.35, 95% CI: 0.15-0.79). In the adjusted occupational model, high quantitative demands (OR = 2.15, 95% CI: 1.21-3.88), high work pace (OR = 2.21, 95% CI: 1.25-3.98), high job insecurity (OR = 2.56, 95% CI: 1.39-4.82), high work life conflict (OR = 5.08, 95% CI: 2.75-9.64) and high job satisfaction (OR = 0.43, 95% CI: 0.20-0.88), high self-rated health (OR = 0.32, 95% CI: 0.17-0.56) were significant. CONCLUSION: This study provides preliminary evidence regarding the factors associated with burnout in MLT. Additional research is needed to understand their relationship with workers health and well-being and in the delivery of health services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".