Trauma Informed Care Training in Ob/Gyn Residency Programs [26G]
Bibliographic record
Abstract
INTRODUCTION: One in six American women experience sexual abuse in their lifetime, which pre-disposes a large percentage of our patients to post-traumatic stress disorder. Patients report being re-traumatized when undergoing exams during childbirth and routine Ob/Gyn visits. Very little is known about whether Ob/Gyn physicians are trained to care for patients who have experienced trauma. METHODS: This study was a cross-sectional survey administered to program directors at all US and Canadian Ob/Gyn Residency programs in September of 2019. RESULTS: At this time, 58 out of 241 (24%) program directors have responded to the survey and represent all ACOG districts. Over 20% of programs have formal training that occurs every year, 63.8% have had some training occur in the past though not on a regular basis, and 15.5% have never had any training. When asked about the primary barrier to providing this training, 27.6% of respondents cited the lack of facilitators to teach it, followed by lack of time within the residency curriculum (17.2%). About 30% of respondents are satisfied with the current training provided at their program. All respondents agree that Ob/Gyn residents need to be trained in trauma informed care, 60.3% believe it should be a CREOG educational objective, and 88% would be somewhat or extremely likely to implement a training program specifically designed for Ob/Gyn residents if one became available. CONCLUSION: Most Ob/Gyn residency programs are providing some training on the care of patients who have experienced trauma, however respondents in our sample are largely unsatisfied with how this occurs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".