Community and healthcare team partnership addressing financial worries as a barrier to health
Notice bibliographique
Résumé
Background: A ground-breaking collaborative project called Reducing the Impact of Financial Strain, intentionally built a foundation through trust, shared purpose, and co-design, to support complex, inter-related health and social issues and system transformation. Who is it for? A diverse group of community stakeholders had a common goal of financial wellbeing as a determinant of health for the community. Primary care teams also worked together with communities to create services that respond to the needs of patients with financial strain. Who did you involve and engage with? In Alberta, Canada four participating rural community multi-sectoral teams worked with their Primary Care Networks to reduce financial strain as a barrier to health using co-design tools such as patient journey mapping, personas, asset mapping, simulations and collaborative decision making. The multi-sectoral community teams were supported with training, funding, collaborative learning conversations, community engagement tools, evidence-based strategies, and evaluation from provincial and regional partners including Alberta Health Services and Alberta Medical Association. Local health promotion and primary care leads supported multisectoral, interdisciplinary teams to co-create financial well-being goals and actions. Multi-sectoral partners included primary care, health and social organizations, municipalities, faith groups, indigenous communities, police, mental health, businesses, nonprofit groups, community agencies and community members. What did you do? Primary care teams brought empathy and financial well-bring into health care conversations by asking patients a question– ‘do you ever have difficulty making ends meet at the end of the month’? By understanding experiences of people living with financial strain, their mindsets shifted and interactions with patients became more meaningful. With the community, they mapped and connected patients to the assets. Some teams hosted an intense and transformational poverty simulation exercise. What results did you get? 623 patients were screened for financial strain, targeting select populations. About 30% screened positive, and 80% of this group accepted a referral for financial assistance, mental health, medication assistance, and social isolation. Most clinic team members embraced the need to talk about financial strain, and agreed that screening for financial strain is important, relevant to practice, and easily implemented. What impact did you have? Collectively, communities implemented 60 local initiatives inspired by evidence-based strategies in the Building Financial Well-being Toolkit, such as technology lending programs, transportation vouchers, system navigation, communities of practice, networking between clinic and community, and financial strain screening and interventions embedded into primary care practice. What is the learning for the international audience? Key learning that teams highlighted included: expand the health team into community, access deep wisdom of community and partners, look for ways to give power to communities and collaborate and co-design innovation solutions with diverse stakeholders. What are the next steps? Other health care and community partners have been inspired by the stories and discoveries shared at https://financialwellness.healthiertogether.ca/ website. A movement towards an integrated health-creating system has been started as primary care teams realize the unrecognized opportunity of collaborating with diverse partners. Primary care teams across the province are beginning to co-design solutions to complex social challenges.
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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 | 0,007 | 0,010 |
| 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 | 0,010 | 0,003 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 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 ».