Clinical Dietitians Identify a Need for Mentorship in Diabetes
Bibliographic record
Abstract
Purpose: Dietitians play a key role in diabetes care. Offering mentorship to increase the capacity of dietitians to provide overall diabetes care may improve outcomes and experiences of people living with diabetes (PWD). The objectives of this study are to assess the perceived need of dietitians for mentorship in diabetes and their preferences for mentorship structure and content. Methods: A 28-question online survey was developed, piloted with 7 dietitians through cognitive interviewing, and disseminated to dietitians in Quebec, Canada. Quantitative data were analyzed descriptively, and survey responses were stratified by years of clinical experience. Results: Of 284 respondents (97% women, mean age 41 ± 10 years), 97% (n = 275) identified a need for mentorship in diabetes. Desire to participate as a mentee, mentor, or both was dependent on years of clinical experience. Formal mentorship was preferred by 41% of respondents, informal by 30%, and 29% preferred a combination of both. The type of mentorship was independent of the years of experience. Finally, 94% believed their confidence levels in providing care for PWD would increase if they participated in mentorship. Conclusion: Mentorship in diabetes was perceived favourably and is believed to increase dietitians’ confidence level in caring for PWD and for interprofessional collaboration.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".