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Record W7161996775 · doi:10.82308/19977

Is there a need for mentorship in diabetes for dietitians? A cross-sectional study using a 28-question survey across the province of Quebec

2024· dissertation· en· W7161996775 on OpenAlexaboutno aff
Sarah Blunden

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipDiabetes mellitusHealth professionalsHealth careDescriptive statisticsComputer-assisted web interviewingMEDLINE

Abstract

fetched live from OpenAlex

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% preferred a combination of both. Type of mentorship was independent of the years of clinical experience. The top diabetes topic identified for mentorship was antihyperglycemic agents: definitions, doses, adjustments, interactions, etc. Finally, 94% believed their confidence level in providing care for people living with diabetes would increase if they participated in mentorship. Conclusion: The need for mentorship specific to dietitians in Quebec was unknown. After analysis, there is a clear need for mentorship, and it has been identified to potentially help increase confidence levels, at the patient care level and inter-professional level irrespective of years of clinical experience. The goal of increasing dietitians' confidence, through mentorship, is to provide care for more people living with diabetes. The need for mentorship for other topics and conditions was also noted. As dietitians gain confidence and play active roles in providing care in their communities, this in turn has the potential to grow the profession of dietetics

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.130
GPT teacher head0.499
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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