Implementation of targeted screening for poverty in a large primary care team in Toronto, Canada: a feasibility study
Notice bibliographique
Résumé
BACKGROUND: Poverty has a significant influence on health. Efforts to optimize income and reduce poverty could make a difference to the lives of patients and their families. Routine screening for poverty in primary care is an important first step but rarely occurs in Canada. We aimed to implement a targeted screening and referral process in a large, distributed primary care team in Toronto, Ontario, Canada. The main outcome was the proportion of targeted patients screened. METHODS: This implementation evaluation was conducted with a large community-based primary care team in north Toronto. The primary care team serves relatively wealthy neighborhoods with pockets of poverty. Physicians were invited to participate. We implemented targeted screening by combining census information on neighborhood-level deprivation with postal codes in patient records. For physicians agreeing to participate, we added prompts to screen for poverty to the charts of adult patients living in the most deprived areas. Standardized electronic medical record templates recommended a referral to a team case worker for income optimization, for those patients screening positive. We recorded the number and percentages of participants at each stage, from screening to receiving advice on income optimization. RESULTS: 128 targeted patients with at least one visit (25%) were screened. The primary care team included 86 physicians distributed across 19 clinical locations. Thirty-four physicians (39%) participated. Their practices provided care for 27,290 patients aged 18 or older; 852 patients (3%) were found to be living in the most deprived neighborhoods. 509 (60%) had at least one office visit over the 6 months of follow up. 25 patients (20%) screened positive for poverty, and 13 (52%) were referred. Eight patients (62% of those referred) were ultimately seen by a caseworker for income optimization. CONCLUSIONS: We implemented a targeted poverty screening program combined with resources to optimize income for patients in a large, distributed community-based primary care team. Screening was feasible; however, only a small number of patients were linked to the intervention Further efforts to scale and spread screening and mitigation of poverty are warranted; these should include broadening the targeted population beyond those living in the most deprived areas.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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.
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