Is there a need for mentorship in diabetes for dietitians? A cross-sectional study using a 28-question survey across the province of Quebec
Bibliographic record
Abstract
Background: Dietitians in Quebec, Canada, who are active members of ODNQ (l'Ordre des diététistes-nutritionnistes du Québec) now have the rights to adjust antihyperglycemic agents and insulin for people living with diabetes (after completing an online training and exam).In response to the current diabetes increase need for care, dietitians have a key role to play.Mentorship in diabetes for dietitians might be an option to help build or solidify confidence levels, grow the profession and with the overall goal of helping more people living with diabetes.Mentorship for healthcare professionals is used to offer guidance, knowledge and experience transfer, advice, and counseling from a mentor to a mentee.The literature has shown mentorship to help build confidence levels, increase retention rates, job satisfaction and growth.However, mentorship for dietitians in Quebec is currently lacking.The primary objective of this study is to assess if there is a need among dietitians for mentorship in diabetes.The secondary objective is to gather insight and information on the structure and content for an appropriate mentorship program for dietitians.Methods: A 28-question online survey was developed, piloted with 7 dietitians through cognitive interviewing, and shared with dietitians across the province of Quebec with help from ODNQ.Descriptive analysis was used to determine the proportions (%) in survey responses, stratified by years of clinical experience.Results: From the 284 participants (97% women, mean age 41+/-10 years), 97% (275) identified a need for mentorship in diabetes.The desire to participate and in what function (mentee, mentor, or both) was dependent on the years of clinical experience of the dietitians who responded.Formal mentorship was preferred by 41% of the respondents, informal by 30% and 29%
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".