An Integrated Methodological Approach to Address Immigrants’ Complex Health Issues: Lessons From the CAN-HEAL Study
Bibliographic record
Abstract
Community-based participatory research (CBPR) and integrated knowledge translation (IKT) are methodological approaches that emphasize the value of equitable partnerships between researchers and community partners. The main difference between these approaches is that CBPR is advocacy-centered and aims at addressing inequities by instigating systemic and policy changes, whilst IKT is application- and upstream solution-oriented especially within the context of health and social care improvement. Previous studies that have used a collaborative approach mainly focused on either CBPR or IKT to a lesser extent.The CAN-HEAL project employed an innovative methodological approach that integrates CBPR and IKT to address mental health needs among Arab immigrants and refugees in Ontario, Canada. Integral to this approach are three pillars: (1) establishment of a multi-level community partnership; (2) adherence to cultural sensitivity principles; and (3) commitment to social justice and application. The use of an CBPR-IKT approach led to numerous successes, including the co-development of a holistic upstream-downstream-based health promotion action plan to tackle inequities. This approach was associated with different challenges (e.g., limited resources), which were mitigated by employing certain enablers (e.g., assistance from community leaders). Based on lessons from this project, recommendations are made for governmental agencies and academic institutions to advance CBPR-IKT research to promote the sustainability and well-being of communities.
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 | 0.234 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".