Que alterações de aprendizado tiveram os residentes de obstetrícia e ginecologia durante a COVID–19?
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic had a significant impact on medical education, including the training of residents, affecting in-person teaching and leading institutions to adopt distance learning methods. Objective: To evaluate the perception of residents in gynecology and obstetrics regarding the impact of the pandemic on their learning, identifying their safety when providing care and seeking to investigate whether residents would consider extending their residency. Methods: A questionnaire with closed questions and responses on a Likert scale was used, addressing different aspects of medical residency during the pandemic to meet the objectives. Results: Of the 71 residents, the majority were women (74.65%). Data analysis revealed that surgical practice was affected for the majority of them (85.92%), with the postponement of elective operations in gynecology (97.18%). Regarding practical learning, 42.25% considered it to be partially satisfactory, while 14.08% considered it unsatisfactory. In the theoretical field, residents’ perception was better, with 43.66% considering the learning satisfactory and 47.89% partially so. The pandemic partially affected medical residency for the majority of residents (85.92%), and alternatives were adopted to replace the lack of theoretical classes and practical activities. Conclusion: The pandemic had a negative effect on medical education and resident training. The interruption of face-to-face activities affected both practical and theoretical learning
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".