Community and healthcare team partnership addressing financial worries as a barrier to health
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".