Integrating case management for patients with complex needs in the ground practice: the importance of context in evaluative designs
Bibliographic record
Abstract
Responding to complex needs calls for integrating care across providers, settings and sectors. Among models to improve integrated care, case management demonstrates a good evidence base of facilitating the appropriate delivery of healthcare services. Since case management is a complex, multi component intervention, with its component parts interacting in a non-linear manner, effectiveness is largely influenced by the context in which the intervention is implemented. This paper discusses how to respond to implementation challenges to evaluating complex interventions for patients with complex needs. Building on the example of case management, we suggest that documenting innovation effectiveness remains important, but that evaluation needs to include theory-based and systems perspectives. We also suggest that implementation science needs to be part of intervention design while engaging stakeholders to define the most relevant research questions and implementation effectiveness, to optimize successful implementation and sustainability.
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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 | 0.108 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".