Mental Health Outcomes among Electricians and Plumbers in Ontario, Canada: Analysis of Burnout and Work-Related Factors
Bibliographic record
Abstract
(i) Background: Working in the electrical and plumbing sectors is physically demanding, and the incidence of physical injury and work disability is high. This study aimed to assess the mental health and well-being of skilled trades workers working in the electrical and plumbing sectors; (ii) Methods: Forty participants completed an online survey assessing burnout, work-related factors, and mental health issues. Data were analyzed to determine the association between demographics, the availability, and importance of work-related factors, and burnout using a two-sample Mann-Whitney U test; (iii) Results: Our findings showed that among the work-related factors, workplace safety, family commitments, income and benefits, and full-time employment opportunities might be crucial factors to keep study participants working at their current position. Financial support for external training, which was found to be the most important factor in preventing colleague-related burnout, was available to the satisfaction of approximately 50% of the participants; (iv) Conclusion: Work-related factors such as workplace safety and the availability and support for external training may be protective against all types of burnout among this population. Future studies may consider a larger sample size with a more diverse group of participants and perform an intersectional analysis to incorporate minority identities in the analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".