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Record W2189613178

Teaching pharmacotherapeutics to family medicine residents

2008· article· en· W2189613178 on OpenAlexvenueno aff
Jana Bajcar, Natalie Kennie‐Kaulbach, Karl Iglar

Bibliographic record

VenueCanadian Family Physician · 2008
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumAccreditationFormative assessmentMedical educationMedicineCurriculum developmentSession (web analytics)NursingFamily medicinePsychologyComputer sciencePedagogy
DOInot available

Abstract

fetched live from OpenAlex

PROBLEM BEING ADDRESSED Medication prescribing is becoming increasingly complex, and the need for formal curricula in pharmacotherapeutics and medication prescribing in accredited family medicine residency programs has been advocated. OBJECTIVE OF PROGRAM The main objective of the pharmacotherapeutic curriculum is to support the development of family medicine residents’ pharmacotherapeutic knowledge and medication prescribing skills required for rational prescribing. PROGRAM DESCRIPTION The curriculum has 4 main components: 1) a medication prescribing framework based on the main tasks and key decisions related to the prescribing of medications, 2) 12 pharmacotherapeutic topics identified in the needs assessment, 3) a 5-step process for session design used by the curriculum development team, and 4) a description of specific roles of facilitators involved in delivering the curriculum. Formative evaluation of the curriculum using resident focus groups has helped to inform the further development of its components. CONCLUSION A formalized curriculum was created to build knowledge of pharmacotherapeutics and effective medication prescribing skills, which are necessary for the current complex environment of patient care and medication management.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.976

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.177
GPT teacher head0.399
Teacher spread0.222 · 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 designNot applicable
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

Citations4
Published2008
Admission routes1
Has abstractyes

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