Implementation of a clinical trial navigation program for cancer patients: Barriers and facilitators identified through stakeholder perspectives.
Notice bibliographique
Résumé
1608 Background: Patient navigation has been highlighted as a solution to improve clinical trial access. The Clinical Trial Navigator (CTN) Program is a Canadian cancer clinical trial navigation program that can be accessed online by patients or healthcare professionals (HCP). Trained individuals search and provide patients and/or oncologists a report of potentially eligible trials for free. Over 550 patients have used the Program since its launch in 2019, but systemic implementation within cancer centers has yet to occur. We aimed to identify facilitators and barriers to implementing the CTN Program in Canadian cancer centers by gathering insights from key stakeholders. Methods: Thirty-three 45-minute, virtual, semi-structured interviews were conducted with healthcare/clinical research professionals (CRP; n = 9) and patient-focused stakeholders (n = 24). Interviews were guided by the Consolidated Framework for Implementation Research (CFIR) and analyzed by two independent researchers using thematic analyses with deductive and inductive coding. Results: Participants highlighted the importance of patient navigation to address barriers related to the limited availability of clinical trials and difficulty in identifying them, noting that navigation can significantly reduce this workload. CRP: “ We need a program dedicated to look at trials across the board. [The clinical trial unit team] has no time or tools to be able to do this for patients.” Key barriers to implementing navigation were the financial and logistical stressors for patients who may want to enroll onto trials that the navigator finds, particularly when only available in another institution. HCP: “[Our province] covers only travel for the consultation, so [financing] is a big barrier and needs to be thought through.” Another commonly cited barrier was obtaining the required medical information for the CTN Program to perform high quality clinical trial searches. Cancer advocacy group leader: “It's got to be very physician structured because [the CTN Program intake form] needs patient records. I’ll ask patients what stage they are at and they don't know, so asking them for their medical information [to perform a clinical trial search], they just don't know that.” When the clinical trial search is initiated by patients and the report of potential eligible trials returned to them, patients felt they needed extra support in discussing the report with their oncologist. Patient: “Every oncologist is different. Some are very easy to talk to...one was extremely difficult...so to have a discussion is very difficult.” Conclusions: Our findings provide critical considerations for the successful implementation of the CTN Program in cancer centers across Canada. We have planned program adaptations to address these results and will evaluate changes in uptake and effectiveness of the CTN Program.
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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 | 0,050 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».