Examining the Mental Health, Wellbeing, Work Participation and Engagement of Medical Laboratory Professionals in Ontario, Canada: An Exploratory Study
Bibliographic record
Abstract
Objectives: The overall objective of this proposed project is to examine the impact of the COVID-19 pandemic on the mental health, functioning and wellbeing of medical laboratory technologists (MLT) and medical laboratory technicians/assistants (MLT/A) in Ontario, Canada. Methods: A cross-sectional study included a self-reported questionnaire for MLT and MLT/A in Ontario. The questionnaire included questions about demographics and occupational characteristics. Questions about mental health, functioning, well-being and psychosocial work environments were also included using validated questionnaires. Results: There were 551 MLT and 401 MLT/A in the analytic sample. Most of the respondents were women. The mean age and standard deviation of the overall sample were 42.0 ± 11.8. MLT demonstrated higher quantitative demands, possibilities for development, and organizational justice compared to MLT/A. The scores of work pace, emotional demands, role conflicts, job insecurity, insecurity over working conditions and negative acts were higher for MLT/A than MLT. The WHODAS 2.0 scores of the respondents were 20.80 ± 6.68, higher than approximately 92% average people. For both groups, most respondents scored the COPSOQ-III domains as worse since COVID-19. Conclusion: The study provides preliminary evidence regarding the workplace mental health outcomes of medical laboratory professionals in Ontario, Canada. The findings suggest that MLT and MLT/A experience psychosocial work conditions that impact mental health, functioning and disability. Accordingly, additional research is necessary to understand the experiences of medical laboratory professionals.
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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 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".