Predictors of Unmet Traditional, Complementary and Alternative Medicine Need Among Persons of Sub-Saharan African Origin Living in the Greater Toronto Area
Bibliographic record
Abstract
Our study seeks to examine how chronic health status, insurance coverage and socioeconomic factors predict unmet traditional, complementary and alternative medicine (TCAM) needs among immigrants from sub-Saharan African origin living in the Greater Toronto Area (GTA). The data for the study comes from a cross-sectional questionnaire survey of 273 sub-Saharan African immigrants living in the GTA. ~ 21% of respondents surveyed had unmet TCAM needs in the 12-month period prior to the survey. Persons with chronic health conditions, lower socioeconomic status, and those with previous history of TCAM use before immigrating were more likely to have unmet TCAM need. The study suggests that the current TCAM healthcare environment in the GTA limits that ability of sub-Saharan immigrants to meet their healthcare needs, especially persons in most need of such treatments-persons with chronic health conditions and those of lower socioeconomic background.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".