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Using the Literature in Developing McGill’s Guidelines for Interactions between Residents and the Pharmaceutical Industry

2004· article· en· W1973614446 on OpenAlexaboutno aff
Ashley Wazana, Annette Granich, François Primeau, Nadeem H. Bhanji, Maya Jalbert

Bibliographic record

VenueAcademic Medicine · 2004
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationPharmaceutical industryMedicineGuidelineFamily medicinePsychologyPedagogyPharmacology

Abstract

fetched live from OpenAlex

Evidence suggests that the pharmaceutical industry exerts a large influence on residents' education and practice. Yet existing guidelines by professional bodies do not cover the specifics of residents' interactions with the pharmaceutical industry. At the psychiatry residency program of the McGill University Health Center, the authors set out to systematically evaluate areas of concern for residents and to develop guidelines for use by residents during and outside their training. Areas of concern included educational activities, training, fundraising, and other specific resident-industry interactions. In 1998, a committee of residents and faculty systematically evaluated areas of concern and, based on a review of the literature and discussions with experts, in 2000 developed guidelines for use by McGill's psychiatry program residents. The process for guideline development and methods for their implementation in 2001 are described. Education and training of residents on resident-industry interactions were included early in the curriculum. Guidelines were developed to address limitations on fundraising activities; restriction of direct gifts to residents; the appropriateness and awarding of industry fellowships; and the handling of drug samples, meals, and other presentations to residents. While guidelines for residents are useful adjuncts for guiding residents' interactions with the pharmaceutical industry, the authors conclude that they need to be reinforced with education and sensitization by faculty.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.116
metaresearch head score (Gemma)0.294
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.616

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1160.294
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0200.008
Science and technology studies0.0030.005
Scholarly communication0.0050.006
Open science0.0080.006
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0020.002

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 head0.773
GPT teacher head0.667
Teacher spread0.105 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainMethods
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

Citations12
Published2004
Admission routes1
Has abstractyes

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