Bibliographic record
Abstract
INTRODUCTION: Little is currently known about how and why consumers choose to use natural health products (NHPs), such as herbs and vitamins. OBJECTIVE: The objective of this study was to explore how the product attributes of NHPs and conventional pharmaceutical sleep aids are linked to consequences and values in consumers' decision making. METHODS: During the spring and summer of 2007, 60- to 90-minute semistructured, laddering interviews based on the means-ends chain approach were conducted with 25 participants experiencing sleep problems in Toronto, Canada, who were selected to have a range of demographic characteristics. RESULTS: Participants varied considerably in the complexity of their decision processes, as between 3 and 14 attribute-consequence-value associations were elicited per interview. The factors found to be most important in determining the type of sleep aid chosen by consumers were whether the product was natural or chemical, whether it was perceived to work or have side effects, and participants' perceptions of the impact of product use on their relationships and, subsequently, on their quality of life. Participants described making different tradeoffs between product attributes (e.g., naturalness) and perceived consequences (e.g., efficacy and side effects) depending on the situational context and indicated that these tradeoffs were done in an effort to maximize values such as overall quality of life. CONCLUSIONS: The naturalness and associated perceived lack of side effects of a product were more important than perceived efficacy for consumers selecting sleep aids for regular use. Only in special cases where efficacy was deemed essential (e.g., prior to important life or work events) did efficacy become a more important factor in the decision-making process.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".