Bibliographic record
Abstract
Objectives: To describe the impacts of the COVID-19 pandemic on family planning training at obstetrician gynecologists (Ob-Gyn)residencies with a Ryan Training Program in Abortion and Family Planning Methods: The Ryan Program (RP) supports Ob-gyn residencies to integrate family planning training. Since 1999, 101 RPs have been established in the US and Canada. In April 2020, questions were added to online surveys for residents and RP directors, asking how the COVID-19 pandemic affected training. Results: Between April 2020 and March 2021, 178 residents completed post-rotation surveys (72%) and 76 RP directors completed annual surveys (94%). Forty-four residents (32.8%) reported that their family planning training was affected by the pandemic. Of those, 34% described a shortened rotation, 32% said training was limited in some way, and 14% were pulled from the rotation to cover other clinics. Approximately 10% described missing the rotation entirely. Eleven residents (25%) were unable to train at collaborating clinics because they were closed temporarily to outside learners. Eighteen percent said patient care was changed from in-person visits to telehealth appointments. Nearly all RP directors (98%) reported that abortion care was considered an essential service by hospital leadership, yet 30% reported training was shortened or limited in some way. Another 29% reported the rotation was halted entirely for some period of time. Conclusions: The COVID-19 pandemic affected family planning training, and some residents missed out on some or all family planning training. Program directors should ensure that their residents with inadequate training have additional support to become competent.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.740 | 0.517 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".