Reducing health and cancer disparities: an environmental scan of three promising health care access interventions in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Cancer incidence and mortality in Canada have improved greatly over recent decades, but these improvements have not been equally distributed across all populations. This environmental scan synthesizes evidence from the real-world implementation of three interventions identified to improve access to cancer care for underserved populations-patient navigation, eHealth, and education and counselling-targeting healthcare access disparities in Alberta. It aims to inform local improvements in cancer care access and healthcare access more broadly, while offering insights for other regions in Canada and internationally with similar health equity challenges. METHODS: A comprehensive electronic database search was conducted to identify academic and grey literature from January 2013 to March 2024. Data on initiatives (context, setting, target population, modality, and lessons learned) were extracted, coded thematically, and, where relevant, mapped to the Consolidated Framework for Implementation Research to support synthesis. RESULTS: Twenty-one initiatives involving patient navigation, 17 utilizing eHealth, and 7 inclusive of education and counselling were identified. Multimodal approaches were commonly used across interventions, enhancing flexibility and accessibility. Most initiatives were urban based, suggesting a gap in rural access. Virtual programming has recently expanded, potentially enhancing reach, while education and counselling initiatives remain limited. CONCLUSIONS: Three key lessons applicable to initiatives which aim to address disparities in cancer care were identified. These lessons emphasize the importance of simplifying implementation, securing leadership and stakeholders' support, and fostering partnerships and collaboration for program success.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.026 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".