Dietitians’ Attitudes and Beliefs Regarding Peer Education in Nutrition
Bibliographic record
Abstract
PURPOSE: Peer education (PE) has been used effectively in nutrition; however, research examining dietitians' attitudes regarding PE is lacking. METHODS: An online survey was sent to a random sample of 1198 Dietitians of Canada members to assess attitudes regarding PE by practice area. RESULTS: A representative sample of dietitians by practice area and location was obtained (n = 229; 19%). Their total attitude score (TAS) was 226 ± 26 (mean ± SD) out of 295 (maximum). Community/public health dietitians had significantly higher TASs compared with clinical dietitians (234 ± 23 vs. 221 ± 27, respectively; P = 0.03). Dietitians believed PE to be most useful in community settings (P < 0.001), with cultural groups or adolescents (P < 0.001), and for healthy eating program goals (P < 0.001). The barrier most agreed with was limited financial resources, whereas the highest perceived benefits were social support and experience/employment for participants and peer educators, respectively. Overall, 63% agreed PE is an effective model, and 59% agreed that PE should be used more often in nutrition. CONCLUSIONS: Dietitians have a positive attitude towards PE, with community/public health dietitians having the most positive attitudes. Dietitians believe PE is useful with specific target populations and particular program goals/strategies; however, they could be challenged to consider PE in a greater variety of programs.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".