A Multilevel Framework for Complex Care: A Critical Interpretive Synthesis
Bibliographic record
Abstract
Health systems are poorly equipped to respond to complex health and social needs, which span sectors and diagnoses. This study puts forward a framework for complex care policy. The framework was developed using critical interpretive synthesis, a method for developing theory on the basis of a transparent search and critical analysis of a heterogenous body of the literature. Seventy-three results were included from a systematic search. We suggested that complex needs can be understood as a pattern of unmet needs occurring at the intersection of fragmented health systems and services, multimorbidity, and social marginalization. We proposed a multilevel framework to inform complex care policy design that accounts for each of these issues and their intersections at the individual, service, and system level. We further identified five principles that have relevance at all levels of complex care. Our framework centres clients and their relationships with providers and suggests how services and systems can support client-level interactions. Conceptualizing complex care policy as a multilevel intervention offers a tool for understanding unexpected effects. Further work is needed to test and refine this framework and to contextualize it for particular populations and settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.206 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.013 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".