Relationships between employment status with self-perceived mental and physical health in Canada
Bibliographic record
Abstract
Background The annual cost of mental illnesses in Canada is estimated to be ＄50 billion. Research from other countries have suggested that employment status is associated with mental and physical health. Within the Canadian context, there is a dearth of research on the relationship between employment and mental health. Objective To explore the relationships between age, gender, income, and employment status on mental and physical health. Methods The 2021 Canadian Digital Health Survey dataset was used for this study. Data records, which included responses for the questions on age, gender, income, employment status, mental, and physical health, were used in the analysis. Ordinal logistics regression was applied to investigate the associations that may exist between mental and physical health with the various sociodemographic factors. Descriptive statistics were also provided for the data. Results The total sample size included in the analysis was 10,630. When compared to respondents who had full-time employment, those who were unemployed were more likely to have lower self-perceived mental health (OR: 1.91; 95% CI: 1.55–2.34). Retired respondents were less likely to have worse mental health than respondents who were employed full-time (OR: 0.78; 95% CI: 0.68–0.90). Self-perceived physical health was more likely to be lower for those who were unemployed (OR: 1.74; 95% CI: 1.41–2.14) or retired (OR: 1.28; 95% CI: 1.12–1.48) when compared to respondents employed full-time. The likelihood of worsening mental and physical health was also found to be associated with age, gender, and income. Conclusion Our findings support the evidence that different factors contribute to worsening mental and physical health. Full-time employment may confer some protective effects or attributes leading to an increased likelihood of having improved mental health compared to those who are unemployed. Understanding the complex relationships on how various factors impact mental health will help better inform policymakers, clinicians, and other stakeholders on how to allocate its limited resources.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".