Interprofessional Collaboration in Addressing Diet as a Common Risk Factor: A Qualitative Study
Bibliographic record
Abstract
Background: Unhealthy diet is a common risk factor threatening dental and general health. Conflicting dietary advice persists among different healthcare professions, despite some shared goals, causing mystification to patients and the public. This qualitative study aimed to understand the perspectives of dentists, physicians, and dietitians in targeting unhealthy diet as a common risk factor, their experiences and barriers in addressing conflicting dietary advice, and possible ways for improving cross-professional coordination.Methods and Findings: A purposive sample of 40 dentists, physicians, and dieticians was recruited from different service sectors and joined in semi-structured interviews, which were subjected to thematic content analysis. Participants supported the common risk factor approach and suggested improving cross-professional cooperation by maximizing potentials of multidisciplinary care, engaging auxiliary/allied staff, refining electronic systems, and incorporating cutting-edge communication technologies. Inconsistent dietary advice stemmed from different treatment focuses and lack of mutual understanding and well-followed guidelines. Inconsistencies can be resolved by striking a balance for the patient’s best interest, well-informing patients, respecting patients’ autonomy, acquiring cross-professional knowledge, and conforming to shared guidelines.Conclusions: Views solicited from three healthcare professions endorsed the importance of cross-professional partnership in preventing/managing dietrelated health problems. Educators, professional bodies, and administrators share the responsibility to dispel conflicting health messages and promote better practice in dietary counselling.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".