Predictors of nutrition care process knowledge and use among dietitians internationally
Bibliographic record
Abstract
BACKGROUND: The nutrition care process (NCP) and its associated standardised terminology (NCPT, referred to collectively as NCP/T) forms a problem-solving framework fundamental to dietetic practice. Global implementation would assist in confirming outcomes from dietetic care, but implementation rates have varied between countries. We investigated which factors predict NCP/T knowledge and use among dietetic professionals in an international cohort, aiming to understand how implementation can be strengthened. METHODS: The validated International NCP Implementation Survey was disseminated to dietitians in 10 countries via professional networks. Implementation, attitudes and knowledge of the NCP/T along with workplace and educational data were assessed. Independent predictive factors associated with higher NCP/T knowledge and use were identified using backward stepwise logistic regression. RESULTS: Data from 6149 respondents was used for this analysis. Enablers that were independent predictors of both high knowledge and frequent use of NCP/T were peer support, recommendation from national dietetic association and workplace requirements (all p < 0.001). Country of residence and working in clinical settings (p < 0.001) were demographic characteristics that were independent predictors of high knowledge and frequent use of NCP/T. A high knowledge score was an independent predictor of frequent NCP/T use (p = 0.002). CONCLUSIONS: Important modifiable enablers for NCP knowledge and use rely on organisational management. National dietetic organisations and key stakeholders such as employers are encouraged to integrate active NCP/T support in their leadership initiatives. This could take the form of policies, formalised and structured training strategies, and informatics initiatives for the integration in electronic health records.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".