When aspirations and achievements don't meet. A longitudinal examination of the differential effect of education and occupational attainment on declines in self-rated health among Canadian labour force participants
Bibliographic record
Abstract
BACKGROUND: To examine the association of a mismatch between educational qualifications and occupational attainment and subsequent declines in self-rated health (SRH) in a longitudinal nationally representative Canadian population sample. METHODS: This study used longitudinal data from 4045 healthy, working respondents of the Canadian National Population Health Survey. Respondents were categorized as either qualified, overqualified, or underqualified based on the match between their education and the skills required for their current occupation over a 2-year period. Logistic regression analysis estimated the odds of decline in SRH over the following 4-year period, using the match between occupation and education as the main independent variable. Analyses were controlled for a number of confounding variables including health behaviours, mental health, self-esteem, job control, and demographic information. RESULTS: Relative to respondents with university education working in occupations for which they were qualified, respondents with university education, working in occupations for which they were overqualified had a significant risk of decline in SRH between 1996 and 2000, even after adjusting for a number of potential confounders (OR = 2.08, 95% CI 1.11-3.91). In respondents with secondary education or less, differences in occupational attainment were not associated with differences in the odds of decline in SRH. CONCLUSIONS: The effect of occupational attainment on health is important for individuals who have invested the most time in their education. Conversely, differential occupational attainment is not associated with differences in the odds of decline in health for participants with lower levels of education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".