Stratification in Post-Secondary Education and Self-Rated Health among Canadian Adults
Bibliographic record
Abstract
Two-thirds of Canadian adults have post-secondary credentials, ranging from trade certificates to bachelor’s and advanced degrees. Yet, little is known about health across these levels, partly because the extensive literature on the education–health gradient has often grouped all post-secondary credentials into one or two broad categories. This is an important gap because it obscures social stratification at the post-secondary level. We provide the first comprehensive study of health across educational attainment levels in Canada, focusing on detailed post-secondary credentials. Data from the 2014–2016 Canadian General Social Survey for adults aged 25 years and older are used to assess self-rated health as a function of educational credentials for the total population and major population groups in relative and absolute terms, and to examine potential mechanisms that could explain the observed patterns. Analyses reveal substantively large, statistically significant differences in health across post-secondary credential levels: the predicted probability of reporting very good or excellent health is 49 percent among adults with trade certificates but 66 percent among those with advanced degrees. Such differences are evident in most although not all population groups. Taking into account social, economic, health–behavioural, and other covariates attenuates the post-secondary credential–health gradient by about 60 percent. Our findings highlight the importance of stratification in post-secondary credentials and the resulting health disparities. Understanding the reasons and implications of these disparities is important for educational, health, and social justice policies.
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 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 | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".