Teaching primary care obstetrics: insights and recruitment recommendations from family physicians.
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".