(080) A SEXUAL HEALTH CLINIC IN AN ONCOLOGY SETTING: PATIENT UPTAKE AND ENGAGEMENT
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».