Sexual Medical Education Challenges During the COVID-19 Pandemic: Strategies for Academic and Community Based Clinicians
Bibliographic record
Abstract
Optimal medical care requires a foundation of competent and experienced healthcare professionals in sufficient numbers, armed with expertise across a wide range of therapeutic disciplines, providing timely care to those in need. To achieve these ideal outcomes, adequate infrastructure, resources, and sufficient numbers of well-trained individuals must be available at all times. The impact of the COVID-19 pandemic on sexual medicine education and training has been particularly challenging. The perceived elective nature of the therapeutic area resulted in a relatively greater loss of care and training during the most recent pandemic compared to other therapeutic areas. Sexual Health education suffered significant cuts during the COVID-19 pandemic as a consequence of reduced patient encounters, canceled educational events, conferences, and in-person interactions. In this report, we review the impact of the pandemic on sexual medicine education focusing on male and female sexual concerns. While diverse opinions exist on the origins of COVID-19, there appears to be consensus that another pandemic is likely in all of our futures. To this end, we have targeted the recognized recent gaps in education and highlight the potential approaches to mitigate such losses that future pandemics may pose.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".