Why and how do consumers use dietary supplements? A systematic review and thematic analysis
Bibliographic record
Abstract
Using the input-process-output (IPO) model as an organizing framework, this paper provides an overview of the literature on multiple dimensions of dietary supplement (DS) consumption. Based on a systematic review of 174 studies from different disciplines over two decades, we pinpointed the key topics that have been examined in this growing body of literature and identified the major themes within DS consumption. Moreover, we integrated the major themes in inputs, processes, and outputs to develop a comprehensive understanding of individuals' DS consumption behaviors, motivations, and social mechanisms underlying such behaviors. This research has implications for health consumption and promotion research. We identified DS consumption as a social-cultural process in which individuals practice lifelong self-learning and socialization activities through which lay health knowledge may be developed. Accordingly, their DS use motives may be related to health improvement or not (e.g. identity expressions), which are formed during interactions with various stakeholders in the health marketplace. When confronting the contemporary health promotion culture and self-care paradigm, it is important for researchers to deepen our knowledge of consumers' motives for DS consumption and the underlying social mechanisms to aid consumers' health consumption decisions and guide the multiple stakeholders in the health marketplace.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".