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Record W2767990067 · doi:10.2196/mededu.7361

The Perceptions of Medical School Students and Faculty Toward Obesity Medicine Education: Survey and Needs Analysis

2017· article· en· W2767990067 on OpenAlexvenueno aff
Mary Metcalf, Karen Rossie, Katie Stokes, Bradley Tanner

Bibliographic record

VenueJMIR Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsObesityOverweightBody mass indexMedicineFamily medicineMedical educationGerontologyPerceptionPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: ). Physicians can address the health impacts of obesity; yet research has suggested that physicians-in-training frequently fail to recognize obesity, are not properly educated regarding treatment options, and spend relatively little clinic time treating obesity. Medical school is a unique opportunity to address this area of need so that the doctors of tomorrow are prepared to treat obesity appropriately. OBJECTIVES: The objective of this study was to determine perceptions of where clinical training for medical students on the topic of obesity and its treatment should improve and expand so that we could address the needs identified in a computerized clinical simulation. METHODS: We conducted a literature review, as well as a needs analysis with medical school students (N=17) and faculty (N=12). Literature review provided an overview of the current state of the field. Students provided input on their current needs, learning preferences, and opinions. Faculty provided feedback on current training and their perceptions of future needs. RESULTS: Most students were familiar with obesity medicine from various courses where obesity medicine was a subtopic, most frequently in Biochemistry or Nutrition, Endocrinology, and Wellness courses. Student knowledge about basic skills, such as measuring waist circumference, varied widely. About half of the students did not feel knowledgeable about recommending weight loss treatments. Most students did not feel prepared to provide interventions for patients in various categories of overweight/obesity, patients with psychosocial issues, obesity-related comorbidities, or failed weight loss attempts. However, most students did feel that it was their role as health professionals to provide these interventions. Faculty rated the following topics as most important to supplement the curriculum: patient-centered treatment of weight, bringing up the topic of weight, discussing weight and well-being, discussing the relationship between weight and comorbidities, and physician role with overweight or obese patients. CONCLUSIONS: A review of the literature as well as surveyed medical students and faculty identified a need for supplementation of the current obesity medicine curriculum in medical schools. Specific needed topics and skills were identified.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.007
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.118
GPT teacher head0.587
Teacher spread0.469 · 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

Labeled directly by 2 models reading the full record.

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

Citations45
Published2017
Admission routes1
Has abstractyes

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