Experiences of family medicine residents in primary care obstetrics training.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Obstetrical practice by family physicians has been declining rapidly for many reasons over the past number of decades. One reason for this trend is family medicine residents not considering intrapartum care as part of their future careers. Decisions such as this may be related to experiences during obstetrical training. This study explored the experiences of family medicine residents in core primary care obstetrics training. METHODS: Using qualitative approaches, focus groups of family medicine residents were conducted. The resulting data were audiotaped and transcribed verbatim. Independent and team analysis was both iterative and interpretive. RESULTS: Data obtained from the focus groups revealed findings relating to the following categories: (1) perceived facilitators to practicing primary care obstetrics, (2) perceived barriers to practicing primary care obstetrics, and (3) learner experiences at the fulcrum of career decision making. CONCLUSION: Family medicine residents were encouraged by favorable learning experiences and group shared-call arrangements by their primary care obstetrics preceptors. Some concerns about a career including obstetrics persisted; however, positive experiences, including influential fulcrum points, may inspire family medicine residents to pursue a career involving primary care obstetrics.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".