Understanding the role of scientific evidence in consumer evaluation of natural health products for osteoarthritis an application of the means end chain approach
Bibliographic record
Abstract
BACKGROUND: Over 30% of individuals use natural health products (NHPs) for osteoarthritis-related pain. The Deficit Model for the Public Understanding of Science suggests that if individuals are given more information (especially about scientific evidence) they will make better health-related decisions. In contrast, the Contextual Model argues that scientific evidence is one of many factors that explain how consumers make health-related decisions. The primary objective was to investigate how the level of scientific evidence supporting the efficacy of NHPs impacts consumer decision-making in the self-selection of NHPs by individuals with osteoarthritis. METHODS: The means-end chain approach to product evaluation was used to compare laddering interviews with two groups of community-dwelling Canadian seniors who had used NHPs to treat their osteoarthritis. Group 1 (n=13) had used only NHPs (glucosamine and/or chondroitin) with "high" scientific evidence of efficacy. Group 2 (n=12) had used NHPs (methylsulfonylmethane (MSM) and/or bromelain) with little or no scientific evidence supporting efficacy. Content analysis and generation of hierarchical value maps facilitated the identification of similarities and differences between the two groups. RESULTS: The dominant decision-making chains for participants in the two scientific evidence categories were similar. Scientific evidence was an important decision-making factor but not as important as the advice from health care providers, friends and family. Most participants learned about scientific evidence via indirect sources from health care providers and the media. CONCLUSIONS: The Contextual Model of the public understanding of science helps to explain why our participants believed scientific evidence is not the most important factor in their decision to use NHPs to help manage their osteoarthritis.
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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 | 0.079 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".