(080) A SEXUAL HEALTH CLINIC IN AN ONCOLOGY SETTING: PATIENT UPTAKE AND ENGAGEMENT
Bibliographic record
Abstract
Abstract Introduction Sexual health issues pose significant and widespread challenges for individuals undergoing cancer treatment. Unfortunately, sexual healthcare clinics are the exception in cancer centres, underscoring the need for effective and efficient sexual health programming in oncology. Providing sexual healthcare through a traditional in-clinic approach is challenging in the current healthcare context of limited resource. Digital health interventions may offer efficient and accessible care pathways. Despite indications of the effectiveness and efficiency demonstrated by digital health innovation in cancer survivorship, patient engagement remains a significant limiting factor. Consequently, establishing digital health programming in cancer care requires the prioritizing of engagement strategies to enhance patient acceptance and encourage active participation in the intervention. This study details the development and patient engagement of a hybrid Sexual Health Clinic (SHC), integrating both in-person and virtual services, within a high-volume cancer centre. Objective The SHC offers broad-spectrum medical, psychological, and interpersonal care through an innovative blended in-person and digital clinic. The objectives of this study are to assess patient uptake and engagement during the first year of operation of the SHC within an oncology setting. Methods The study is conducted in a high-volume oncology Centre situated in a large urban setting. Participants encompass patients referred to the SHC by their oncology team between January 1st, 2023, and December 31st, 2023. The implementation of the SHC adhered to the Quality Implementation Framework. Furthermore, well-established patient engagement strategies in the online context were employed, including validation of product credibility and security, usability testing with improved functionality, training for both participants and providers, facilitation by practitioners, customization of information, guided usage, feedback mechanisms for patients and providers, and reminder features. A structured patient monitoring system was utilized to track SHC uptake, while virtual care engagement was gauged through website usage and analytics. Descriptive statistics were employed to summarize the collected data. Results The structured implementation approach resulted in 381 referrals in 2023. Of those patients referred to SHC, 23 (6%) never responded and 44 (11%) were contacted but were not interested, leaving 311 (82%) patients with intention to attend the SHC. To date, of those patients with intention to attend the SHC in-person clinic, 247 (82%) presented in clinic and a remaining 34 patients referred in late 2023 may still be scheduled (allowing for a possible 90% participation rate). Evaluation of patient engagement in the SHC virtual clinic revealed that the 178 eligible participants are very active on the platform, registering 1867 messages between patients and health counsellors, 1845 trackers completed, and an average of 247 “information slides” read per patient. Overall satisfaction with SHC is 4.1 on 5 point Likert Scale. Conclusions The patient tracking data confirms the effective integration of SHC within a busy oncology centre, while website analytics indicate promising patient involvement on the SHC virtual platform. These findings suggest that SHC holds promise in bridging the sexual health care gap for oncology patients in a effective and potentially resource-efficient manner. Disclosure No.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".