Bibliographic record
Abstract
Out of pocket (OOP) healthcare expenditures can be burdening for persons of low socioeconomic status. Little is known about socioeconomic, demographic, and health disparity in OOP healthcare expenditures in Canada. This thesis examines the trends of OOP healthcare expenditures during the 2004-2015 period in Canadian provinces using microdata files from the Canadian Research Data Center through the University of Northern British Columbia, and describes the association of OOP healthcare expenditures with various socioeconomic, demographic, and pre-existing health factors. It also estimates the contribution of these factors to the share of OOP healthcare expenditures to incomes. Regression results reveal that the share of OOP healthcare expenditures to incomes are negatively related to income, but positively related to old age, being married, larger household sizes, and pre-existing health conditions. Also, OOP healthcare expenditures are generally higher for female Canadians, and for persons residing in the provinces of Quebec, Alberta and New Brunswick.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".