Examining burnout in the electrical sector in Ontario, Canada: A cross-sectional study
Bibliographic record
Abstract
Workers in the trades sectors often experience mental health issues and decreased work ability due to occupational stress, workplace hazards and living in danger or constant fear of injury. Understanding the impacts of psychosocial risk factors on construction workers' mental health can aid in decreasing workplace injuries, lessening disabilities and increasing worker productivity. In this study, we focus on understanding and assessing the mental health and wellness of individuals in the electrical sector that are members of the Employer Engagement Project (EEP) from the Ontario Electrical League (OEL). The subset of potential participants included electricians and plumbers in Ontario working for small to medium sized employers (SME). The recruitment took place in 2022, with a total of 82 participants who completed a survey collecting demographic information, assessing the importance and availability/satisfaction of workplace factors and stress-and burnout-related questions. The data were analyzed using IBM SPSS Statistics 29.0. Two-sample Mann-Whitney U tests were performed to test for associations between the availability of work-related factors and burnout scores among the participants. Burnout scores were determined using the Copenhagen Burnout Inventory. Our findings demonstrate that dissatisfaction of the following factors: Workload allocation, internal staff development opportunity and stable staffing/minimal turnover, were associated with high burnout levels. The findings indicate there may be a relationship between certain work-related factors and burnout levels experienced. There is a need for improvement of workload allocation in SMEs to help enhance the mental health and well-being of employees.
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 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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".