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Record W1976709628 · doi:10.1080/13561820802012976

Integrating social accountability into continuing education and professional development at medical schools: The case of an institutional collaborative project in Canada

2008· article· en· W1976709628 on OpenAlexaffabout
Joanne Goldman, Scott Reeves, Helen Novak Lauscher, Sandra Jarvis-Selinger, Ivan Silver

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British ColumbiaThe Wilson CentreUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsAccountabilitySocial accountingProfessional developmentMedical educationContinuing professional developmentPublic relationsPolitical scienceInterprofessional educationPublic administrationMedicineBusinessHealth care

Abstract

fetched live from OpenAlex

Social accountability is playing an increasingly significant role in medical education across Canada. This paper presents findings from a mixed methods evaluation of a collaboration project - Issues of Quality and Continuing Professional Development: Maintenance of Competence (CPDiQ) - undertaken by all 17 Canadian medical schools to promote social accountability in continuing professional development/continuing medical education programs. Data were gathered at three stages during the project to explore project participants' views and experiences of collaboration. Findings indicated there were four main benefits of this national collaboration: promoting a focus on social accountability; maximizing resources; enabling local learning; and developing a trusting foundation. Two key difficulties were identified: uncertainties about goals of collaboration; and communication challenges. CPDiQ was one important step amongst the many sustained, multifaceted initiatives required, to advance social accountability as a key goal of medical schools. The leadership within CPDiQ and provided by a national medical organization was instrumental in this initiative.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0000.000

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 head0.012
GPT teacher head0.374
Teacher spread0.361 · 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 designQualitative
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

Citations13
Published2008
Admission routes2
Has abstractyes

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