A comparative analysis of integrated comprehensive care models: Lessons Canada can learn from East Africa
Bibliographic record
Abstract
OBJECTIVES: This study investigates integrated comprehensive care (ICC) models in different geographical contexts reflecting diverse population needs. Using a target innovation profile framework, it describes fundamental ICC building blocks to create a universally adaptable ICC model. This mode presents opportunities for enhancing public health service delivery in Canada and East Africa. STUDY DESIGN: A descriptive comparative study that uses qualitative methods to determine critical success factors of ICC models. METHODS: Researchers completed a literature review of 14 international ICC models and validated findings through surveys with 29 healthcare professionals from Canada, the United States, Uganda, the Democratic Republic of the Congo, and Australia. Interviews were conducted with 7 health professionals to deepen insights for ICC in East Africa and Canada. RESULTS: Literature indicated that timely accessibility, patient and family involvement, partnerships with community partners, a single healthcare team, resources in preventative care, and consideration of social determinants of health were essential aspects of ICC models. Surveys and interviews highlighted opportunities to increase preventative care resource allocation and to improve community-level health accessibility in the Canadian context through knowledge sharing from East African advanced community care approaches. CONCLUSION: By establishing the essential elements of ICC models and differences across geographic contexts, we demonstrate a specific example where knowledge mobilization efforts would enhance public health systems globally. With integration of health systems being of interest globally, there is a compelling reason to continue learning from each other to enhance health service delivery.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".