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Record W2481695084

Teaching primary care obstetrics

2014· article· en· W2481695084 on OpenAlexaffvenueabout
Sudha Koppula, Judith Belle Brown, John Jordan

Bibliographic record

VenueCanadian Family Physician · 2014
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPrimary careMedicineObstetrics and gynaecologyQualitative researchMedical educationFamily medicineObstetricsNursingPsychologyPregnancy
DOInot available

Abstract

fetched live from OpenAlex

Objective To explore the experiences and recommendations for recruitment of family physicians who practise and teach primary care obstetrics. Design Qualitative study using in-depth interviews. Setting Six primary care obstetrics groups in Edmonton, Alta, that were involved in teaching family medicine residents in the Department of Family Medicine at the University of Alberta. Participants Twelve family physicians who practised obstetrics in groups. All participants were women, which was reasonably representative of primary care obstetrics providers in Edmonton. Methods Each participant underwent an in-depth interview. The interviews were audiotaped and transcribed verbatim. The investigators independently reviewed the transcripts and then analyzed the transcripts together in an iterative and interpretive manner. Main findings Themes identified in this study include lack of confidence in teaching, challenges of having learners, benefits of having learners, and recommendations for recruiting learners to primary care obstetrics. While participants described insecurity and challenges related to teaching, they also identified positive aspects, and offered suggestions for recruiting learners to primary care obstetrics. Conclusion Despite describing poor confidence as teachers and having challenges with learners, the participants identified positive experiences that sustained their interest in teaching. Supporting these teachers and recruiting more such role models is important to encourage family medicine learners to enter careers such as primary care obstetrics.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.231
Teacher spread0.219 · 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 designOther design
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

Citations0
Published2014
Admission routes3
Has abstractyes

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