Case management in primary care for people with complex care needs: a realist evaluation
Bibliographic record
Abstract
Context: Abundant literature supports case management (CM) as an intervention to improve care for people with complex care needs, but little explains how and why CM is effective, or not, in particular contexts. Objective: To understand and explain how CM in primary care for people with complex care needs works, under what conditions and for whom. Study design: Realist evaluation. Setting: Seven primary care clinics across 4 provinces in Canada. Population studied: People with chronic conditions and complex care needs. Intervention: CM led by case managers in partnership with patients and other healthcare professionals according to four core components: 1) Patient needs assessment; 2) Care planning, including individualized services plan; 3) Care coordination; 4) Self-management support. Methods. Data collection: realist interviews with patients (n=13), case managers (n=6), clinic managers (n=3) and other healthcare professionals (n=8). Analysis: Context (i.e. background of the intervention), mechanism (i.e. reasoning, attitudes and behaviors of stakeholders) and outcome (i.e. intervention impact) (CMO) were identified in a comprehensive table for each interview. CMO were then aggregated, organized and interpreted by the team. Regular discussions with the team helped to refine and validate the CMOs. Results: Fourteen CMOs that demonstrate the mechanisms triggered to drive outcomes in particular contexts were identified. CM was most likely to be successful if all stakeholders were engaged in the intervention and felt supported by managers and colleagues. Moreover, positive patient effects were likely to be reported if trusting relationships were developed between patients and case managers. Differences in CM effectiveness across clinics were related to patient characteristics (motivation and complexity of healthcare needs), case manager characteristics (experience, background, skills and attitude) as well as organizational factors (access to care and external support, time and providers workload, team culture, and work location). Positive impacts on family members were also observed when they felt supported, respected, and accepted. Conclusions: The realist evaluation offer context-sensitive explanations to better inform local practices and policies and to contribute to improved health of patients with complex care needs.
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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 | 0.049 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".