<i>Rural Consumers’ Attitudes</i>: Towards Nutrition Labelling
Bibliographic record
Abstract
PURPOSE: Consumer workshops in rural and remote locations were evaluated for their efficacy in changing participants' self-perceived attitudes and behaviours related to nutrition labelling. METHODS: Project-trained community health educators used pilot-tested workshop resources to facilitate 18 workshops across the country. Participants completed pre-workshop questionnaires to permit the identification of demographic characteristics and attitudes and behaviours related to nutrition labelling at point-of-purchase. RESULTS: The majority of the 259 consumers who submitted questionnaires were women (81%), and aged 35 to 54 (35%); 51% reported more than a high school education and 34% had less than $25,000 as a yearly family income. Self-perceived attitudes and behaviours related to nutrition labelling differed only slightly by family income before the workshop. Workshops were rated positively (mode=4 [range 2 to 5]). Thirty-five consumers were surveyed three months after the workshop; the majority were women (89%), were aged 35 to 54 (43%), and had completed high school (51%). Self-perceived attitudes and behaviours for all respondents (n=35) had improved. Use of acquired knowledge and skills at point-of-purchase was high for all respondents (mode=4 [range 2 to 5]; five-point Likert scale). CONCLUSIONS: Providing in-person consumer workshops with pilot-tested materials in rural and remote locations had positive impacts on attitudes and behaviours related to the use of nutrition labelling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".