<i>Dietitians’ Opinions and Experiences</i>Of Client-Centred Nutrition Counselling
Bibliographic record
Abstract
PURPOSE: The concept of "client-centredness" was explored within a nutrition counselling relationship. METHODS: A two-round reactive Delphi survey was used. The first survey was sent to 65 Dietitians of Canada members who indicated in the member database that they had advanced counselling skills. Following analysis of the data, the second-round questionnaire was developed and sent to participants with a report of the first-round results. Analysis of the second-round survey indicated that participants' responses had remained stable, and the Delphi survey was terminated. RESULTS: Participants agreed that most of the issues identified in the Delphi questionnaire should be included in a client-centred approach to practice; however, when participants were asked about their experience in these areas, median responses and/ or the interquartile ranges changed, indicating some difficulty in implementing the client-centred approach. Comments also indicated that the reality of their workplaces did not allow participants to be as client-centred as they thought they should be, and suggested that the concept of "client-centredness" is not universally understood by dietitians. CONCLUSION: If a client-centred approach to practice is truly important, we need to start a dialogue within the profession to gain a deeper understanding of what this means and how it can be implemented.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".