Multisectoral partnerships to tackle complex health issues at the community level: lessons from a Healthy Communities Approach in rural Alberta, Canada
Bibliographic record
Abstract
SETTING: Health inequities exist in rural communities across Canada, as rural residents are more likely than their urban counterparts to experience injuries, chronic conditions, obesity, and shorter life expectancy. Cooperative and coordinated action across sectors is required to both understand and address these complex public health issues. INTERVENTION: The Alberta Healthy Communities Approach (AHCA) is based on the values and core building blocks of the Healthy Communities Approach, a framework centred on building community capacity to support community-led actions on the determinants of health. Adaptations within the AHCA focused on implementation mechanisms with a 5-step process and supporting implementation and assessment tools for multisectoral team building. Local measurement of change was enhanced and focused on community capacity and multisectoral action stages. Between 2016 and 2019, the AHCA was piloted with 15 rural communities across Alberta with population sizes ranging from 403 to 15,051 people. OUTCOMES: While communities piloting the AHCA ranged in the level of diversity of their coalition membership and partnerships, members' reflections demonstrate that intentional engagement with diverse citizens and sectors is pivotal to collaboratively identifying local assets and priorities and mobilizing cross-sectoral action that will sustainably improve supportive environments for cancer and chronic disease prevention. IMPLICATIONS: Engaging across sectors, building partnerships, and establishing a multisectoral team increase diversity and can catalyze community-led prioritization and actions for asset-based community development. An increase in diversity may lead to increased investment and sustainability at the community level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".