Enhanced skills training in family medicine maternity care: Cross-sectional study of graduates' experiences.
Bibliographic record
Abstract
OBJECTIVE: To describe the experiences of participants in Canadian family medicine maternity care enhanced skills programs: their current practice situation with respect to maternity care; the reasons they pursued enhanced maternity care training; and their perceptions of competencies attained during the program. DESIGN: Cross-sectional questionnaire. SETTING: Canada. PARTICIPANTS: Graduates of family medicine enhanced skills programs in maternity care in Canada between 2004 and 2014. MAIN OUTCOME MEASURES: Participants' current engagement in intrapartum care; reasons for participating in the enhanced skills programs; interest in obstetrics at different points in training; and development of maternity care competencies during both core residency and the enhanced skills program. RESULTS: Eighty-seven graduates (response rate of 44%) participated. At an average of 5 years in practice, 77% of enhanced skills graduates were providing intrapartum care. Sixty-nine percent of respondents took the enhanced skills program because they did not feel ready to practise obstetrics without supervision. More than half (55%) of respondents had intended to include obstetrics in their future practices when they were in medical school. By the end of residency, 99% intended to practise obstetrics; however, this percentage decreased to 87% by the end of fellowship. There was a statistically significant increase in graduates' perceptions of various maternity care competencies (eg, vacuum-assisted birth, perineal repair) following enhanced skills training. Eighty-two percent of respondents indicated that the ability to access enhanced skills training supported their decision to provide obstetrics care. CONCLUSION: This is the first evaluation of graduates of enhanced skills programs in maternity care in Canada. Enhanced skills programs appear to support the education of family medicine maternity care providers; however, these programs might be compensating for residents' lack of confidence in providing maternity care independently rather than providing truly enhanced skills. This study also confirms that some medical students and family medicine residents change their minds in the direction of wanting to provide full-scope maternity care during the course of their education.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".