Associations between mental health and sociodemographic and work-related factors in Canadian SME workers: an observational study
Bibliographic record
Abstract
BACKGROUND: Small and medium-sized enterprises (SMEs) employ a considerable proportion of the workforce in many countries. However, little research has specifically explored how SME employees in Canada fare in terms of mental health. This study aimed to (1) draw a detailed portrait of employees’ mental health using positive (i.e., flourishing, work well-being) and negative (i.e., psychological distress, burnout) mental health indicators, (2) examine how these indicators’ scores vary according to sociodemographic (e.g., gender) and work-related (e.g., SME size) factors, (3) examine workers’ perceptions of the COVID-19 pandemic’s impact on their general mental health and work well-being, and (4) identify mental health profiles of SME workers based on these mental health indicators, and determine which sociodemographic and work-related factors predict profile membership. METHODS: Two thousand five hundred SME workers (18+, living in Canada, working at least 14 h/week at a private SME) who were members of a large online panel participated in an online survey, in which they answered mental health measures (e.g., personality disorder, burnout, post-traumatic stress, flourishing). RESULTS: 54.70% of SME workers reported having at least one mental health issue, 40.38% met the cut-off for psychological distress, and 22.13% met the criteria for burnout. Workers also reported moderately high work well-being and flourishing. Less than half of workers reported a negative impact of COVID-19 on their general (47.07%) and work-related (33.41%) mental health. Proportionally more older workers reported a perceived negative or neutral effect of COVID-19 on their general and work mental health; more women reported a perceived negative impact of the pandemic on their general mental health. Four latent mental health profiles were found. Individual- (gender, age) and organizational-level (SME size, economic sector) factors predicted mental health scores and profile membership. CONCLUSIONS: This study provides a comprehensive overview of Canadian SME workers’ mental health. Management should prioritize primary, secondary, and tertiary occupational health interventions in SMEs to increase work well-being and flourishing, with particular attention given to at-risk workers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".