Process Evaluation of the North York CARES (Community Access to Resources Enabling Support) Integrated Care Program for Complex Older Adults
Bibliographic record
Abstract
Introduction: In 2020, North York Community Access to Resources Enabling Support (NYCARES) was launched as a new hospital-to-home integrated care program for older adults requiring an alternate level of care. This process evaluation described and assessed NYCARES by its contexts, implementation conditions, and mechanisms of impact. Methods: Data were program documentation, field notes, implementation metrics, and stakeholder interviews. Quantitative and qualitative data were analyzed descriptively and thematically, then interpretively synthesized. Logic models were developed to describe the program theory of change and tested to assess implementation. Results: Coordinated, wraparound care by an intersectoral team was an expected mechanism. An unexpected mechanism was the care navigator who facilitated multiple program processes. Implementation challenges involved decision-making relationships among teams and timely operationalization of program decisions. Discussion: Logic modelling demonstrated the program’s evolution from design through real-world implementation. Unexpected mechanisms may arise due to implementation issues like a lack of clarity on target populations and program processes. Process evaluation findings can be incorporated into a refined theory of change for evaluation of program effectiveness. Conclusion: Alignment among program teams is critical when delivering new integrated care programs. Such programs require optimization of specific and unexpected contextual and operational factors as the design evolves.
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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 | 0.046 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".