<i>Linking Active Living and Nutrition Counselling:</i>Dietitians’ Perceptions
Bibliographic record
Abstract
Healthy eating and active living have become key concepts in health promotion, and, increasingly, the two messages are being combined. Dietitians are in an ideal position to promote physical activity as an adjunct to nutrition counselling. Focus group interviews were used to identify dietitians' perceptions and needs related to incorporating active living messages into their practices. Six focus groups, involving 42 participants (40 registered dietitians and two dietetic interns), were conducted in cities throughout Alberta. Issues explored during the interviews included the dietitian's role in promoting active living, barriers to promoting physical activity, opportunities for collaboration between dietitians and exercise professionals, and dietitians' perceived needs for integrating active living messages into their practices effectively. Findings indicate strong support for incorporating active living messages into nutrition counselling. However, lack of knowledge was identified as a barrier to dietitians' proactive role, and concerns were raised about public and professional perceptions if dietitians counselled on this issue. Participants felt that they required additional knowledge and training in physical activity in order to promote it effectively. Development and implementation of programs designed to meet these needs would help dietitians integrate active living messages into their practices.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".