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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".