Pre-Implementation Assessment of a Sexual Health eClinic in Canadian Oncology Care
Bibliographic record
Abstract
Sexual dysfunction is a prevalent and often under-addressed concern among prostate cancer survivors, significantly affecting quality of life for patients and their partners. The True North Sexual Health and Rehabilitation eClinic (SHAReClinic) is a virtual, biopsychosocial intervention developed to improve access to sexual health support for prostate cancer survivors and their partners. This study used a qualitative descriptive design to examine barriers and facilitators influencing the integration of SHAReClinic into oncology care across nine Canadian health care centres. Semi-structured interviews were conducted with 17 knowledge users, including health care providers and institutional leaders. Data were analyzed using a hybrid deductive-inductive thematic approach guided by the Consolidated Framework for Implementation Research (CFIR) 2.0. Participants described SHAReClinic as a much-needed resource, particularly in the absence of standardized sexual health pathways in oncology care. The virtual format was seen as accessible and well suited to addressing sensitive topics. However, limited funding, lack of institutional support, and workflow integration challenges emerged as primary barriers to implementation. Findings offer practical, theory-informed guidance for integrating SHAReClinic into oncology care and highlight key considerations for developing sustainable and scalable survivorship care models.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".