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Record W2111373060 · doi:10.36834/cmej.36571

GP Surgeons’ Experiences of Training in British Columbia and Alberta: A Case Study of Enhanced Skills for Rural Primary Care Providers

2012· article· en· W2111373060 on OpenAlexaffvenueabout
Jude Kornelsen, Stuart Iglesias, Nancy Humber, Nadine R. Caron, Stefan Grzybowski

Bibliographic record

VenueCanadian Medical Education Journal · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsMentorshipMedical educationAttritionContext (archaeology)CurriculumMedicineTraining (meteorology)Qualitative researchContinuing medical educationNursingExploratory researchPsychologyContinuing educationPedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: There has been a steady erosion of family physicians with enhanced surgical skills providing care for rural residents. This has been largely due to the lack of formal training avenues and continuing medical education (CME) opportunities afforded to those interested, and attrition of those currently practicing. METHODS: A qualitative study was undertaken using an exploratory policy framework to guide the collection of in-depth interview data on GP surgeons' training experiences. A purposive sample of GP surgeons currently practicing in rural BC and Alberta communities yielded interviews with 62 participants in person and an additional 8 by telephone. Interviews were audio recorded and transcribed then subjected to a process analysis. RESULTS: Participants thematically identified motivations for acquiring advanced skills training, resources required (primarily in the area of solid mentorship), the most efficacious context for a training program (structured), and differences in mentorship between obstetricians and general surgeons. CONCLUSION: Mentors and role models were the most salient influencing factor in the trajectory of training for the participants in this study. Mentorship between specialists and generalists was constrained at times by inter-professional tensions and was accomplished more successfully within a curriculum-based, structured environment as opposed to a learner-responsive training environment.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.388
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 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

Citations8
Published2012
Admission routes3
Has abstractyes

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