564-P: The Need for Mentorship in Diabetes Is Now
Bibliographic record
Abstract
Background: Mentorship for health care professionals is used to offer guidance, knowledge, and transfer experience. Clinical Dietitians (CD) play a key role in diabetes care. Offering mentorship might help build their confidence levels (CL) and grow the profession with the goal of improving outcomes and experiences for people living with diabetes (PWD). The objectives are, in CD (1) to assess the need for mentorship in diabetes and (2) to gather opinions on the structure and content for appropriate mentorship. Methods: A 28-question online survey was developed, piloted with 8 CD through cognitive interviewing, and shared with CD across the province of Quebec (Canada). Descriptive analysis was used to determine the proportions (%) in survey responses, stratified by years of clinical experience (CE). Results: From the 284 participants (97% women, mean age 31+/-10 years), 275 (97%) of CD identified a need for mentorship in diabetes. Desire to participate and in what function (mentee, mentor or both) was dependent on the years of CE. Formal mentorship was preferred by 41%, 30% informal and 29% a combination of both which was independent of the years of CE. Finally, 93.5% believe their confidence level in providing care for PWD would increase if they participated in mentorship. Conclusion: Mentorship in diabetes was perceived as needed to increase Clinical Dietitians confidence level in caring for PWD and for interprofessional collaboration. Disclosure S.Blunden: Employee; Dexcom, Inc. A.Roy-fleming: None. C.South: None. C.Gillis: None. A.Brazeau: Other Relationship; Dexcom, Inc., Diabète québec, Ordre des diététistes nutritionnistes du Québec, Research Support; Canadian Institutes of Health Research, Fonds de recherche du Québec en Santé.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".