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Medical education in substance‐related disorders: components and outcome

2000· review· en· W2012382528 on OpenAlexafffund
Nady el‐Guebaly, John Toews, Jocelyn Lockyer, Susan Armstrong, David C. Hodgins

Bibliographic record

VenueAddiction · 2000
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of CalgaryCentre for Addiction and Mental Health
FundersUniversity of CalgaryU.S. Department of Health and Human Services
KeywordsPsychological interventionInclusion (mineral)Substance abuseSpecialtyPsychologyMedical educationAddictionOptimismCognitionContinuing medical educationMedicineContinuing educationPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

AIMS: To analyze the process of acquisition by physicians of a body of knowledge and skills in the management of substance abuse. DESIGN: A comprehensive search of English-speaking literature was conducted over 20 years. Articles assessing the outcome of educational strategies in undergraduate, graduate and continuing medical education were examined to determine the targeted sample, the educational strategies involved and the outcomes assessed. FINDINGS: Nine studies in undergraduate education, 11 in graduate and 11 in continuing education met the inclusion criteria. They were generally difficult to compare in design, strategy and outcome analysis. Cognitive knowledge and behavioral skills appear to be easier to obtain compared to more complex attitudinal shifts. CONCLUSIONS: There is growing consensus in the selection of a combined didactic and interactive educational strategy but few empirical data as to the more cost-effective learning interventions. Training must be reinforced at regular intervals. While the expanding panoply of interventions available to physicians should enhance the perceptions of role legitimacy and treatment optimism, cohort studies across levels of education, specialty groups and across-substance and other addictive behaviors are required to determine cost-effective educational strategies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.034
GPT teacher head0.347
Teacher spread0.313 · 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 designOther design
Domainnot available
GenreReview

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

Citations60
Published2000
Admission routes2
Has abstractyes

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