Implementing shared decision-making in nutrition clinical practice: A theory-based approach and feasibility study
Bibliographic record
Abstract
BACKGROUND: There are a growing number of dietary treatment options to choose from for the management of many chronic diseases. Shared decision making represents a promising approach to improve the quality of the decision making process needed for dietary choices that are informed by the best evidence and value-based. However, there are no studies reporting on theory-based approaches that foster the implementation of shared decision making in health professions allied to medicine. The objectives of this study are to explore the integration of shared decision making within real nutritional consultations, and to design questionnaires to assess dieticians' intention to adopt two specific behaviors related to shared decision making using the Theory of Planned Behavior. METHODS: Forty dieticians will audiotape one clinical encounter to explore the presence of shared decision making within the consultation. They will also participate to one of five to six focus groups that aim to identify the salient beliefs underlying the determinants of their intention to present evidence-based dietary treatment options to their patients, and clarify the values related to dietary choices that are important to their patients. These salient beliefs will be used to elaborate the items of two questionnaires. The internal consistency of theoretical constructs and the temporal stability of their measurement will be checked using the test-retest method by asking 35 dieticians to complete the questionnaire twice within a two-week interval. DISCUSSION: The proposed research project will be the first study to: provide preliminary data about the adoption of shared decision making by dieticians and theirs patients; elicit dieticians' salient beliefs regarding the intention to adopt shared decision making behaviors, report on the development of a specific questionnaire; explore dieticians' views on the implementation of shared decision making; and compare their views regarding the implementation of shared decision making in different clinical settings. It is anticipated that the results generated by the proposed research project will significantly contribute to the emergence of shared decision making in nutrition through a theory-based approach.
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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 | 0.052 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".