What influences success in family medicine maternity care education programs? Qualitative exploration.
Bibliographic record
Abstract
OBJECTIVE: To ascertain how program leaders in family medicine characterize success in family medicine maternity care education and determine which factors influence the success of training programs. DESIGN: Qualitative research using semistructured telephone interviews. SETTING: Purposive sample of 6 family medicine programs from 5 Canadian provinces. PARTICIPANTS: Eighteen departmental leaders and program directors. METHODS: Semistructured telephone interviews were conducted with program leaders in family medicine maternity care. Departmental leaders identified maternity care programs deemed to be "successful." Interviews were audiorecorded and transcribed verbatim. Team members conducted thematic analysis. MAIN FINDINGS: Participants considered their education programs to be successful in family medicine maternity care if residents achieved competency in intrapartum care, if graduates planned to include intrapartum care in their practices, and if their education programs were able to recruit and retain family medicine maternity care faculty. Five key factors were deemed to be critical to a program's success in family medicine maternity care: adequate clinical exposure, the presence of strong family medicine role models, a family medicine-friendly hospital environment, support for the education program from multiple sources, and a dedicated and supportive community of family medicine maternity care providers. CONCLUSION: Training programs wishing to achieve greater success in family medicine maternity care education should employ a multifaceted strategy that considers all 5 of the interdependent factors uncovered in our research. By paying particular attention to the informal processes that connect these factors, program leaders can preserve the possibility that family medicine residents will graduate with the competence and confidence to practise full-scope maternity 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 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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".