Collective case study: integrated health and social care for older adults within a Canadian context
Bibliographic record
Abstract
Purpose Canada's population is aging and there are concerns that the welfare system may not support the increased demands on it. Integrated health and social care (IHSC) produces positive health and system outcomes but it needs to be better understood within a Canadian context. The purpose of this collective case study of three IHSC initiatives in Alberta, Ontario and Nova Scotia was to determine the factors that support successful services integration among different healthcare and social services organizations serving older adults within a Canadian context. Design/methodology/approach This study used the Cheng and Catallo (2020) IHSC conceptual framework (CF) to guide the research. Primary data were based on key informant interviews of representatives from organizations that comprised each case and focus groups. A cross-case analysis was undertaken to determine common themes. Findings The cross-case analysis revealed that the three cases shared common integration and external influence factors based on the Cheng and Catallo (2020) CF. Some new factors were identified. Originality/value The study revealed that the Canadian context was important in influencing integration in the three cases and that there is a unique Canadian aspect to IHSC. The study offers up practical insights for government leaders and service administrators to improve IHSC for older adults. The study also identifies how the Cheng and Catallo (2020) IHSC CF can be enhanced and points to research opportunities to test the framework.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".