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Record W2331883027 · doi:10.1093/alcalc/agu052.52

SY13-1-3 * INTERNATIONAL ADDICTION MEDICINE: EDUCATIONAL AND TRAINING EFFORTS

2014· article· en· W2331883027 on OpenAlexaff
Nady el‐Guebaly

Bibliographic record

VenueAlcohol and Alcoholism · 2014
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedical educationCurriculumAddictionPresentation (obstetrics)Addiction medicineStandardizationPsychologySection (typography)MedicinePolitical sciencePedagogyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Promoting training in Addiction Medicine worldwide has resulted in a number of efforts, two of which will be the topic of this presentation. A. The international meeting on International Addiction Medicine Training (Nijmegen) has the following aims: – Sharing knowledge and experience in the field across various educational stages; – Sharing ideas about the undergraduate and postgraduate curricula concerning knowledge, skills and competencies; – Determine whether and where international standardization is possible. Highlights and recommendations will be reported. B. A parallel effort has been the drafting of an International Textbook. The Education and Training section has nine chapters. A number of conclusions emerge from this Section. The initiatives described are uniformly recent ones and are at various stages of development. Until recently, there was little international awareness of each other's national efforts and it is hoped that the Section will promote more international collaboration and support and may even be a catalyst for long distance learning. We still lack a clear picture of undergraduate education at various medical schools as in many countries each design its own. This is important because almost every medical doctor will be confronted with addicted patients. A well designed curriculum will presumably help to destigmatize addicted patients but will also bring to the fore that addiction medicine can be an interesting field for future doctors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.570

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 head0.112
GPT teacher head0.395
Teacher spread0.284 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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