WA health practitioners and cooking: How well do they mix?
Bibliographic record
Abstract
Abstract Aim: The aim of the present study was to assess the views and experiences of WA practitioners on the use of cooking as a public health nutrition intervention. Methods: A 39‐point online questionnaire was constructed using Survey Monkey. The questionnaire was distributed via email distribution lists targeting practitioners working in public health nutrition. Questions were focused around four objectives relating to: the value of cooking skills in public health, practitioner cooking skills and training, practitioner views on cooking as a health intervention and practitioner experiences in conducting cooking demonstrations. Results: A total of 84 practitioners completed the questionnaire, of which over half (58%) were employed in dietetic specific positions at the time of the survey. There was overwhelming agreement that cooking skills are an important factor in the prevention of nutrition‐related disease, and that cooking skill interventions have the potential to change dietary intakes. However, only one quarter of practitioners indicated that cooking skill interventions were a significant part of their current role. Over half (58%) of the practitioners surveyed had either conducted or assisted in a cooking demonstration or cooking class in the last 12 months. Conclusions: WA practitioners place a high value on the use of cooking as a public health nutrition intervention. Practitioners felt they have good knowledge and skills in cooking but indicated the need to know more about conducting cooking skill interventions. The findings suggest the need to improve outcome evaluation as a component of cooking skill interventions to assess long‐term behaviour change.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".