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Record W2064581384 · doi:10.1186/1472-6920-11-51

A controlled trial of mental illness related stigma training for medical students

2011· article· en· W2064581384 on OpenAlexaff
Aliya Kassam, Morven Leese, Joanne Loughran, Graham Thornicroft

Bibliographic record

VenueBMC Medical Education · 2011
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Calgary
FundersProgramme Grants for Applied ResearchH. Lundbeck A/SKing's College LondonNational Institute for Health and Care ResearchSouth London and Maudsley NHS Foundation Trust
KeywordsMental illnessStigma (botany)Psychological interventionMental healthMedicinePromotion (chess)PsychologyClinical psychologyPsychiatryMedical education

Abstract

fetched live from OpenAlex

BACKGROUND: The evidence base for mental illness related stigma interventions in health care professionals and trainees is underdeveloped. This study aimed to examine the impact of mental illness related stigma training on third year medical students' knowledge, attitudes and behaviour related to people with mental illness. METHODS: A non-randomised controlled trial was conducted with 110 third year medical students at a medical school in England to determine the effectiveness of a mental illness related stigma training package that targeted their knowledge, attitudes and behaviour. RESULTS: We detected a significant positive effect of factual content and personal testimonies training upon an improvement in knowledge, F(1, 61) = 16.3, p = 0.0002. No such difference was determined with attitudes or for behaviour. CONCLUSIONS: Knowledge, attitudes and behaviour may need to be separately targeted in stigma reduction interventions, and separately assessed. The inter-relationships between these components in mental health promotion and medical education warrant further research. The study next needs to be replicated with larger, representative samples using appropriate evaluation instruments. More intensive training for medical students may also be required.

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)0.002
metaresearch head score (Gemma)0.001
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: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.090
GPT teacher head0.468
Teacher spread0.379 · 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 designRandomized trial
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

Citations89
Published2011
Admission routes1
Has abstractyes

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