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Attitudes toward mental illness in medical students: does personal and professional experience with mental illness make a difference?

2000· article· en· W2169328265 on OpenAlexaff
Deborah A. Roth, Martin M. Antony, Kathryn Lawson Kerr, Fiona Downie

Bibliographic record

VenueMedical Education · 2000
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsInstitute for Christian StudiesMcMaster UniversitySt. Joseph's HospitalUniversity of Toronto
Fundersnot available
KeywordsMental illnessMental healthAffect (linguistics)PsychologyInterpersonal communicationClinical psychologyInterpersonal relationshipPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

PURPOSE: Medical students may be susceptible to emotional difficulties because of the high levels of both academic and interpersonal stress associated with their training. This study examined attitudes toward mental illness in medical students. It was expected that people who had experience of mental illness, either in their personal lives or through their professional experience, would have more positive attitudes toward students with mental health problems than would people who had not had such experience. METHOD: Faculty and staff employed by a large American university medical centre completed a questionnaire package including several measures designed to assess specific attitudes toward medical students with emotional problems. Data were also collected on the degree to which specific mental disorders were thought to interfere with the performance of medical students. RESULTS: In general, prior experience with mental illness, either through personal or professional activities, was associated with more positive attitudes about students with mental illness. However, the pattern of findings was complex. CONCLUSION: Future research should examine the extent to which specific mental illnesses actually affect the performance of medical students.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.335
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.000

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 head0.024
GPT teacher head0.448
Teacher spread0.424 · 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

Citations70
Published2000
Admission routes1
Has abstractyes

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