<i>Development of a Nutrition Counselling Care Map</i>For Dyslipidemia
Bibliographic record
Abstract
PURPOSE: Care maps or clinical pathways for nutrition therapy of dyslipidemia could add to current practice guidelines, by providing templates for feasible and recommended diet counselling processes. A care map was therefore developed by engaging expert and generalist dietitians and external experts from across Canada in a multi-stage consensus process. METHODS: First, a qualitative study was undertaken with a convenience sample of 12 practitioners to identify possible diet care options, using hypothetical client scenarios and cognitive analysis. Second, these care options were rated for five case scenarios considered typical (overweight clients, with or without clinical cardiovascular disease and other comorbidities, potentially motivated to change, consuming high-fat diets, and facing various major barriers to eating behaviour change). The rating was conducted through a survey of participants. Highly appropriate, recommended, and feasible options for counselling were ranked through a two-round modified Delphi process, with teleconference discussions between rounds. RESULTS: Forty-nine professionals started the consensus process; 39 (80%) completed all aspects. Numerous care processes were appropriate for all clients, with additional focus on barriers for low-income clients, sodium intake for clients with hypertension, and smoking cessation in smokers. CONCLUSIONS: The resulting care map, "Dietitians' Quick Reference Guide for Clinical Nutrition Therapy for Overweight Clients with Dyslipidemia," provides a basis for current practice and new effectiveness studies.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".