MétaCan
Menu
Back to cohort
Record W1958429297 · doi:10.1080/13548506.2015.1062523

Investigating the feasibility and acceptability of health psychology-informed obesity training for medical students

2015· article· en· W1958429297 on OpenAlexaff
Anna Chisholm, Jo Hart, Karen Mann, Mark Perry, Harriet Duthie, Leila Rezvani, Sarah Peters

Bibliographic record

VenuePsychology Health & Medicine · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedical educationSession (web analytics)Intervention (counseling)FidelityMotivational interviewingBehaviour changePsychologyProtocol (science)MedicineDiseaseWeight managementObesityApplied psychologyAlternative medicineNursingWeight loss

Abstract

fetched live from OpenAlex

Health psychologists have succeeded in identifying theory-congruent behaviour change techniques (BCTs) to prevent and reduce lifestyle-related illnesses, such as cardiovascular disease, cancers and diabetes. Obesity management discussions between doctors and patients can be challenging and are often avoided. Despite a clear training need, it is unknown how best to tailor BCT research findings to inform obesity-management training for future healthcare professionals. The primary objective of this descriptive study was to gather information on the feasibility and acceptability of delivering and evaluating health psychology-informed obesity training to UK medical students. Medical students (n = 41) attended an obesity management session delivered by GP tutors. Sessions were audio-recorded to enable fidelity checks. Acceptability of training was explored qualitatively. Tutors consistently delivered training according to the intervention protocol; and students and tutors found the training highly acceptable. This psychology-informed training can be delivered successfully by GP tutors and further research is warranted to explore its efficacy.

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)0.021
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.411
GPT teacher head0.601
Teacher spread0.190 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePsychology Health & MedicineSame topicBehavioral Health and InterventionsFrench-language works237,207