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Record W2005996824 · doi:10.1300/j069v21n03_07

Physician Behavior Towards Male and Female Problem Drinkers

2002· article· en· W2005996824 on OpenAlexaff
Lynn Wilson, Meldon Kahan, Eleanor Liu, Joan M. Brewster, Mark B. Sobell, Linda C. Sobell

Bibliographic record

VenueJournal of Addictive Diseases · 2002
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsThe Wilson Centre
Fundersnot available
KeywordsChecklistMedicineRating scaleFamily medicineFamily historySocial skillsInterpersonal communicationClinical psychologyPsychiatryPsychologyInternal medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Evidence suggests that physicians are less likely to identify alcohol problems in females than in males. PURPOSE: To compare the performance of family medicine residents with male and female simulated patients (SPs) posing as problem drinkers. METHODS: Fifty-six family medicine residents completed a baseline survey on knowledge and attitudes towards problem drinkers. Each resident was then visited by one male and female unannounced SP. The male and female roles were similar with respect to presenting complaint (in somnia or hypertension), age, social class, and drinking history. RESULTS: Residents expressed slightly more positive attitudes towards female than male patients (3.32 vs. 3.09, p < .001). Residents scored higher with undetected male than with undetected female SPs on the assessment checklist (5.1 vs. 3.2, p < .045), the management checklist (4.4 vs. 3.2, p = .032), and an interpersonal rating scale (the Alcohol Skills Rating Form; 5.5 vs. 4.7, p = .023). CONCLUSION: Educational programs should focus on improving physicians' clinical skills in the identification and treatment of alcohol problems in women.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.274
Teacher spread0.251 · 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.

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

Citations13
Published2002
Admission routes1
Has abstractyes

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