Interventions to improve access to cancer care in underserved populations in high income countries: a systematic review
Notice bibliographique
Résumé
Background: Underserved populations both globally and in Canada face serious cancer inequities that result from systemic economic, environmental, and social conditions. These pose barriers in access to cancer care and lead to suboptimal cancer care experiences and outcomes. Knowledge of effective interventions to improve access to cancer care is needed to inform the design of tailored interventions for these populations. Aim: To identify interventions and programs to improve access to cancer care for underserved populations in high income countries with universal health coverage (UHC) and the United States (US) throughout the cancer care continuum. Methods: We conducted a systematic review following the PRISMA standards. We searched Medline, EMBASE, PsycINFO, CINAHL, Scopus, and the Cochrane Library. Inclusion criteria: quantitative and qualitative studies published in English in the last 10 years (2013-2023), describing interventions/programs to improve access to cancer care for underserved populations (18 years and over). We included studies in the US given the body of scholarship on equity in cancer care in that country. Screening, data extraction and analysis were undertaken by two independent reviewers. Results: Our search yielded 7,549 articles, and 74 met the inclusion criteria. Of these, 56 were conducted in the US, 8 in Australia, 6 in Canada, and 4 in the United Kingdom. Most (90.5%) were quantitative studies and 47.3% were published between 2020-2023. Seven types of interventions were identified: patient navigation, education and counselling, virtual health, service redesign, financial support, improving geographical accessibility and multicomponent interventions. Interventions were mainly designed to mitigate language, distance, financial, lack of knowledge and cultural barriers. Most interventions focused on access to cancer screening, targeted rural populations, racialized groups and people with low socioeconomic status, and were conducted in community-based settings. The majority of interventions or programs significantly improved access to cancer care. Conclusion: Our systematic review findings suggest that interventions designed to remove specific barriers faced by underserved populations can improve access to cancer care. Few studies came from countries with UHC. Research is required to understand tailored interventions for underserved populations in countries with UHC.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».