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Record W3002600829 · doi:10.1186/s12909-019-1908-0

Improving obesity management training in family medicine: multi-methods evaluation of the 5AsT-MD pilot course

2020· article· en· W3002600829 on OpenAlexaff
Thea Luig, Sonja Wicklum, Melanie Heatherington, Albert Vu, Erin Cameron, Doug Klein, Arya M. Sharma, Denise Campbell‐Scherer

Bibliographic record

VenueBMC Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsNOSM UniversityUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedical educationCourse (navigation)MedicineWeight managementTraining (meteorology)ObesityWeight lossEngineeringInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Quality, evidence-based obesity management training for family medicine residents is needed to better support patients. To address this gap, we developed a comprehensive course based on the 5As of Obesity Management™ (ASK, ASSESS, ADVISE, AGREE, ASSIST), a framework and suite of resources to improve residents' knowledge and confidence in obesity counselling. This study assessed the course's impact on residents' attitudes, beliefs, and confidence with obesity counselling. METHODS: The course combines lectures with a bariatric empathy suit experience, standardized and in-clinic patient practice, and narrative reflections. Using a multi-methods design we measured changes in 42 residents' attitudes, beliefs, and self-confidence and thematically analyzed the narrative reflections to understand residents' experience with the course content and pedagogy. RESULTS: Following the course, residents reported improved attitudes towards people living with obesity and improved confidence for obesity counselling. Pre/post improvement in BAOP scores (n = 32) were significant (p < .001)., ATOP scores did not change significantly. Residents showed improvement in assessing root causes of weight gain (p < .01), advising patients on treatment options (p < .05), agreeing with patients on health outcomes (p < .05), assisting patients in addressing their barriers (p < .05), counseling patients on weight gain during pregnancy, (p < .05), counseling patients on depression and anxiety (p < .01), counseling patients on iatrogenic causes of weight gain (p < .01), counseling patients who have children with obesity (p < .05), and referring patients to interdisciplinary providers for care (p < .05). Qualitative analysis of narrative reflections illustrates that experiential learning was crucial in increasing residents' ability to empathically engage with patients and to critically reflect on implications for their practice. CONCLUSION: The 5AsT-MD course has the potential to increase residents' confidence and competency in obesity prevention and management. Findings reflect the utility of the 5As to improve residents' confidence and competency in obesity management counselling.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.403
GPT teacher head0.600
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations30
Published2020
Admission routes1
Has abstractyes

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