Immigrant Usage Patterns of Natural Health Products: Role in Pharmacoeconomics
Bibliographic record
Abstract
Background: Understanding patterns and drivers for natural health product (NHP) usage among immigrants is essential in the provision of appropriate health care; many studies have elucidated NHP utilization among immigrants; however, few have considered impacts of concurrent NHP and prescription medication usage. Objective: The study aims to determine new immigrant NHP usage patterns (including concurrent usage with prescription medications) and to discern economic impacts driving concurrent usage. Methods: A survey questionnaire was administered to local new immigrants during English Language Training classes. Results: Most participants understood the NHP definition and would take an NHP for the same disease or condition they would normally take a prescription medication for. Many participants agreed that NHPs are not safe however were unable to provide robust examples of unsafe NHP usage. With regard to purchases of medicines for short and long term illnesses, a high percentage of participants would purchase the prescription medication for a short term illness over the NHP; however this percentage decreases in the event of a long term illness, with more participants relying on NHPs to remedy their long term illness symptoms. Conclusion: Pharmacoeconomics tends to be a major driver for immigrant utilization of NHPs, and is a stronger influencer of use compared to ethnicity or parenteral usage of such products. This pharmacoeconomic correlation in the preference to use NHPs over prescription medications tends to be more observable for chronic and long term conditions (compared to short term illnesses).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".