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Record W2112628865 · doi:10.3109/01421590903197050

Does hands-on CME in gynaecologic procedures alter clinical practice?

2010· article· en· W2112628865 on OpenAlexafffundabout
Susan P. Phillips

Bibliographic record

VenueMedical Teacher · 2010
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsQueen's University
FundersGovernment of OntarioInstitute for Clinical Evaluative Sciences
KeywordsPessaryPsychological interventionMedicineFamily medicineFamily planningContinuing medical educationExperiential learningHysterectomyClinical PracticeNursingGynecologyMedical educationContinuing educationPsychologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Despite evidence favouring initial medical treatment for benign uterine conditions, hysterectomy rates in Ontario, Canada, in the 1990s were variable, and relatively high. Best methods for translating this or any evidence into practice are, however, elusive. AIM: This study evaluates whether an interactive skill development program had an impact on family practice participants' subsequent ability to manage benign uterine conditions medically. METHODS: Effectiveness of 50 experiential 3 h skill transfer workshops with peers teaching IUD insertion, endometrial sampling and pessary fitting (offered 2005-2007) was assessed by measuring changes in actual practice. Family physicians billing the Ontario Health Insurance Plan over 5 years (2003-2007 inclusive) formed the control group with whom 138 FP workshop participants (cases) were compared. RESULTS: Rates of all procedures increased amongst 138 family physician attendees following participation, but remained unchanged amongst controls. Number of physicians offering the target interventions also increased among cases, but not controls. CONCLUSION: Evidence-based information, delivered by peers, and associated with the opportunity to practice new skills appear to be the components of continuing medical education (CME) that translate into improved clinical care.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
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)0.0100.000

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 head0.028
GPT teacher head0.394
Teacher spread0.367 · 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 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

Citations8
Published2010
Admission routes3
Has abstractyes

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