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Record W4289745418 · doi:10.1080/10872981.2022.2107419

Enhancing interprofessional collaboration and interprofessional education in women’s health

2022· review· en· W4289745418 on OpenAlexaff
Laura Baecher-Lind, Angela C. Fleming, Rashmi Bhargava, Susan M. Cox, Elise Everett, David A. Forstein, Shireen Madani Sims, Helen Morgan, Christopher M. Morosky, Celeste S. Royce, Tammy Sonn, Jill M. Sutton, Scott Graziano

Bibliographic record

VenueMedical Education Online · 2022
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInterprofessional educationSpecialtyMedical educationMedicineHealth careObstetrics and gynaecologyPerspective (graphical)NursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

This article is from the 'To The Point' series from the Association of Professors of Gynecology and Obstetrics Undergraduate Medical Education Committee. The purpose of this review is to provide an understanding of the differing yet complementary nature of interprofessional collaboration and interprofessional education as well as their importance to the specialty of Obstetrics and Gynecology. We provide a historical perspective of how interprofessional collaboration and interprofessional education have become key aspects of clinical and educational programs, enhancing both patient care and learner development. Opportunities to incorporate interprofessional education within women's health educational programs across organizations are suggested. This is a resource for medical educators, learners, and practicing clinicians from any field of medicine or any health-care profession.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.530
Teacher spread0.495 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations23
Published2022
Admission routes1
Has abstractyes

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