COMMUNITY ENGAGED APPROACH TO CANCER CONTROL POLICY IN ABIA STATE - NIGERIA: A MIXED-METHODS ACTION RESEARCH PROJECT
Notice bibliographique
Résumé
Background\nCancers are becoming increasingly common in Nigeria and other developing countries. The most common cancers in the country are those affecting the breast, cervix, and prostate. Beyond the National Cancer Control Plan, most States in Nigeria do not have State cancer control policies which is unlike the situation in Canada and other developed countries. Using the multiple perspective analysis framework, this research sought to explore the perspectives of patients diagnosed with cancer, healthcare providers and health policymakers regarding cancer policy in Abia State.\n\nMethods\nA concurrent mixed methods action research design was used. Sampling included individuals aged ≥18 years who were diagnosed with breast or cervical cancer, provided cancer treatment or made health policy in the State. This study was conducted in collaboration with the Abia Cancer Control Group (ACCG), a community-based coalition of non-governmental organizations, clinicians, and government parastatals. Survey data were collected at the same time as the interviews which occurred following ethical approvals from the University of Saskatchewan’s Behavioural Research Ethics Board and Abia State’s Ministry of Health Human Research Ethics Committee.\n\nResults/Findings\nSurvey participants were 29 patients who had been diagnosed with cancer, 50 health care providers and 33 policymakers (n=112), with an average age of 45 (±11) years. Challenges identified by ≥60% of participants were: lack of local data regarding cancers (95.2%, 79/83); lack of treatment pathways (92.8%, 77/83); absence of support groups for patients (88.0%, 73/83); low public awareness (75.9%, 63/83); and limited availability of treatment options (62.6%, 52/83). Some themes that evolved from the qualitative data were: low cancer awareness; delays in cancer treatment; and, financial burden on patients. The top three priority areas for a new cancer control policy were: cancer prevention (83%, 93/112); State cancer legislation (80%, 86/112); and multi-agency partnerships (79%, 88/112). Most participants (80%, 90/112) recommended that health insurance should fund ≥16% of cancer control activities, although policymakers were more likely to make quarterly insurance contributions than patients (7 out of 10 vs. 5 out of 10). Data from participants that agreed to be interviewed (n=24) were grouped into the following themes: Experiences (e.g. challenges regarding cancer prevention, awareness of early detection, delays in cancer service, and cost of services) and Expectations (e.g. priority rating for cancer control, funding structure, and framework for a future cancer control policy). ACCG provided contextual evidence of the usefulness of these findings by organizing community-driven cancer control projects locally linked to advocacy, training of clinicians, patient navigation and support, as well as developing a centralized cancer reporting system.\n\nConclusion\nCancer control was an important issue for all populations. Inadequate early detection services with a background of >3-month diagnostic delay characterized cancer control in Abia State. Future cancer control policy should emphasize: cancer prevention; the creation of local clinical pathways; and, a blended model for financing cancer control activities. Collaboration with community groups such as ACCG will be critical to the successful development and implementation of a cancer control policy in Abia State.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,005 |
| 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 ».