Native/Aboriginal Students use Natural Health Products for Health Maintenance More so than Other University Students
Bibliographic record
Abstract
Background and aim: University student use of Natural Health Products (NHP) for health maintenance (HealthM) is assessed in Canada.We hypothesize greater use of NHP by Native/Aboriginal and female students.Demographic predictor variables and the top ten NHP used are determined.Methods: A cross-sectional survey of 963 students (n=212 Native/Aboriginal; n=751 non-Native/Aboriginal) was conducted.χ2 and Fisher's exact tests analyzed group differences.Multiple logistic regressions determined predictor variables of NHP use.Results: Of 963 surveyed students, 268 (27.8%) used NHP for HealthM, while 695 students (72.2%) did not.More Native/Aboriginal students used commercial tobacco (47% vs. 13%, P<0.001) and NHP (67% vs. 45%, P<0.001) than non-Native/Aboriginal students.Gender was not associated with NHP use (P=0.527).Canadians used echinacea more than non-Canadians (Odds Ratio.OR=4.96; 95% CI: 1.2-21.0).Ginger (OR=0.39;95% CI: 0.2-0.78)and garlic (OR=0.28;95% CI: 0.13-0.6)were popular amongst non-Canadians.Native/Aboriginal students used homeopathics (OR=39.9;95% CI: 8.6-185.4)and rat root (OR=56.73;95% CI: 6.91-465.8).Chamomile was less used by males (OR=0.33;95% CI: 0.13-0.83)and used more by upperclassmen (OR=2.6 95% CI: 1.3-5.3).Conclusion: Homeopathics and rat root are popular amongst Native/Aboriginal students.Garlic and ginger are popular amongst non-Canadians than Canadian students; however, more Canadians used echinacea for HealthM than non-Canadians.Chamomile is less popular amongst males.Commercial tobacco is used more by Native/Aboriginal students.Predictors of NHP use are: Native/Aboriginaland upperclassman.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".