PB1936: IDENTIFYING INEQUITIES IN SUPPORT - GLOBAL SURVEY OF PATIENT ORGANISATIONS DELIVERING SUPPORT SERVICES FOR CLL PATIENTS
Notice bibliographique
Résumé
Topic: 6. Chronic lymphocytic leukemia and related disorders - Clinical Background: The CLL Advocates Network (CLLAN), a global network of patient advocacy organisations who support those affected by chronic lymphocytic leukemia (CLL), conducted a survey to understand at a global level the provision of support services for patients with CLL, access to healthcare prior to COVID-19 pandemic and the impact of COVID-19 on delivery of CLL treatment and care. Aims: The aim was to collect support service and organisational data to understand and analyse the services and support offered to patients worldwide by CLL organisations. Through service and geographical mapping, identification of strategic priorities to address inequities in lived experience and access to care can be developed. Methods: Desktop research identified 158 patient advocacy organisations in 69 countries that focused specifically on services to support areas of CLL, blood cancer or all cancers. They were invited to take part in an online survey between 6 May and 2 August 2021. The 63-question survey was available in 7 languages. Results: 57 respondents represented patient advocacy organisations in 40 countries. Countries were segmented into low-and-middle-income countries (LMIC) and high-income countries (HIC). LMIC include those classified as •Least developed countries or •Low-income countries or •Lower middle-income countries and territories or •Upper middle-income countries and territories by the Organisation for Economic Co-operation and Development (OECD). Image 1:Overall responses reported most frequent barriers to providing services were lack of human resources/ staff/ volunteers (74% for both HIC and LMIC), lack of financial resources (52% HIC vs. 89% LMIC), lack of time (48% HIC vs. 32% LMIC) and lack of skills and knowledge (19% HIC vs. 37% LMIC). Questions provided insight into the leadership and opportunities that CLLAN could provide to support organisations in their service delivery. While best practice sharing ranked highest for both LMIC (100%) and HIC (83%), overall LMIC ranked the support from CLLAN in higher value to support their practice. This demonstrates a strong demand in LMIC to build their capacity to support their patients with CLL. Other areas that ranked highly included training/courses (e.g., online learning modules) for organisation staff or volunteers with 95% for LMIC and 59% for HIC. Face to face capacity building events/ learning events such as conferences and virtual conferences for capacity building, digital learning and networking were ranked highly by both groups (89% LMIC vs. 64% HIC). Summary/Conclusion: This survey has demonstrated a high level of interest and engagement in networked activities. It has also highlighted the variety of services that organisations are offering to patients and education about CLL more widely. The survey results have identified gaps in service provision and how this differs across organisations and between countries across the globe. Responses provided to this survey show a deficit in services particularly for patients in LMIC. Improved collaboration and communication between all those involved in CLL will improve outcomes for all patients regardless of their geographical location. CLLAN can help support, promote and facilitate this, continuing to increase the offering of specific best practice sharing and capacity building resources. CLLAN and its member organisations need to continue to work in collaboration with healthcare, research and policy makers across the globe to improve the outcomes for all patients with CLL regardless of their location and socioeconomic status. Keywords: Chronic lymphocytic leukemia
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».