The Anatomy of a Hybrid In-Person and Virtual Sexual Health Clinic in Oncology
Bibliographic record
Abstract
Sexual health is compromised by the diagnosis and treatment of virtually all cancer types. Despite the prevalence and negative impact of sexual dysfunction, sexual health clinics are the exception in cancer centers. Consequently, there is a need for effective, efficient, and inclusive sexual health programming in oncology. This paper describes the development of the innovative Sexual Health Clinic (SHC) utilizing a hybrid model of integrated in-person and virtual care. The SHC evolved from a fusion of the in-person and virtual prostate cancer clinics at Princess Margaret. This hybrid care model was adapted to include six additional cancer sites (cervical, ovarian, testicular, bladder, kidney, and head and neck). The SHC is theoretically founded in a biopsychosocial framework and emphasizes interdisciplinary intervention teams, participation by the partner, and a medical, psychological, and interpersonal approach. Virtual visits are tailored to patients based on biological sex, cancer type, and treatment type. Highly trained sexual health counselors facilitate the virtual clinic and provide an additional layer of personalization and a "human touch". The in-person visits complement virtual care by providing comprehensive sexual health assessment and sexual medicine prescription. The SHC is an innovative care model which has the potential to close the gap in sexual healthcare. The SHC is designed as a transferable, stand-alone clinic which can be shared with cancer centers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".