“I rarely read the label”: Factors that Influence Thai Consumer Responses to Nutrition Labels
Bibliographic record
Abstract
BACKGROUND: This qualitative study employed the Knowledge-Attitude-Behaviour (KAB) model and Health Belief Model (HBM) to investigate factors influencing Thai consumer decision making about use of nutrition labels. Labels include both Nutrition Information Panels (1998-) and Guideline Daily Amounts labels (2011-). METHOD: In-depth interviews were conducted with 34 participants representing two socio-demographic extremes in Thailand--"urban Bangkok" (university educated consumers) and "provincial Ranong" (non-university educated consumers). An integrated KAB-HBM model was used to devise in-depth interviews for a qualitative study using 20 open-ended questions and samples of food package labels. Additional questions arose from the interviews and they lasted 30-45 minutes and were video recorded. The analysis identified recurring themes using Atlas.ti software. RESULTS: Most participants (n=25) were aware of nutrition labels but a much smaller number (n=10) used and derived any benefit from them. Nutrition label users were classified into 4 groups: A) competent user; B) confused user; C) aware non-user; D) unaware non-user. Better educated participants were better at understanding nutrition labels but not more likely to use labels. Belief that nutrition influences health increased likelihood of using nutrition labels to make decisions about food. Being well-educated and motivated by health concerns increased likelihood of attention to nutrition labels. CONCLUSION: Results are discussed with a view to increasing the use of nutrition labels by Thai consumers. Our findings, drawing on a combination of the KAB and HBM models, can contribute to strategies motivating consumers to use nutrition labels and can provide useful insights for developing promotional strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".