Exploring the Impact of Labour Mobility on the Mental Health and Wellbeing of Skilled Trades Workers in Ontario, Canada
Bibliographic record
Abstract
Labour mobility and subsequent workers migration is an increasing trend worldwide and can be a force that counteracts Canada's shortage of skilled labour. Supercommuting allows workers facing economic challenges to pursue more financially advantageous work opportunities in other regions. This study aimed to evaluate the "supercommuting" labour mobility model and its impact on long-distance mobile workers' mental health and wellbeing. We utilized a non-experimental research design using convenience sampling from workers who participated in Blue Branch Inc.'s (Hamilton, Canada) supercommuting labour mobility model. An online questionnaire collected demographic data, work-related data, occupational stress measures related to burnout, and job-related stress data. Data collection was started on 1 April 2021, and of the total 58 participants, the majority (44, 76%) were male, born outside Canada, and had an average age of 32.8 years. Workplace Safety (95%), full-time employment opportunity (95%), career advancement possibility (95%), and income and benefits (94.9%) were found to be the most crucial factors to keep study participants working in their current position. Of the 47 participants who experienced burnout, only one showed severe burnout in each domain (personal, work-related, and colleague-related). There is a great need for preventative burnout programs and supportive employer resources for those who engage in long-distance labour commuting. The study emphasizes the need to encourage policymakers to develop solutions for training future Ontario workers to support mobile employment and long-distance labour commuting.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 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".