Psychosocial Factors and Intention to Use the Nutrition Care Process Among Dietitians and Dietetic Interns
Bibliographic record
Abstract
PURPOSE: The theory of planned behaviour was used to explore the factors (i.e., attitude, subjective norm, and perceived behavioural control) affecting the intention of dietetic internship educators, new dietetic graduates, and dietetic interns to use the nutrition care process (NCP) in their clinical practice. METHODS: Participants (n=55) were recruited from the Bachelor of Science in Nutrition program at Université Laval. They completed an online quantitative questionnaire assessing their intention to use the NCP in their clinical practice, as well as associated psychosocial factors. Open-ended questions were also used to gain a further understanding of the salient beliefs underlying participants' intention to use the NCP. RESULTS: Intention to use the NCP in practice and associated psychosocial factors were similar and favourable within the three participant groups. Subjective norm and perceived behavioural control were the psychosocial factors that significantly predicted an intention to use the NCP. The most cited perceived barrier to use of the NCP was a lack of knowledge, while the most cited facilitator was training opportunities. CONCLUSIONS: Our results indicate that successful implementation of the NCP will likely require the development of theoretical and practical training activities for both pre-licensure students and experienced dietitians.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".