Bibliographic record
Abstract
Purpose Models of integrated care are prime examples of complex interventions, incorporating multiple interacting components that work through varying mechanisms to impact numerous outcomes. The purpose of this paper is to explore summative, process and developmental approaches to evaluating complex interventions to determine how to best test this mess. Design/methodology/approach This viewpoint draws on the evaluation and complex intervention literatures to describe the advantages and disadvantages of different methods. The evaluation of the electronic patient reported outcomes (ePRO) mobile application and portal system is presented as an example of how to evaluate complex interventions with critical lessons learned from this ongoing study. Findings Although favored in the literature, summative and process evaluations rest on two problematic assumptions: it is possible to clearly identify stable mechanisms of action; and intervention fidelity can be maximized in order to control for contextual influences. Complex interventions continually adapt to local contexts, making stability and fidelity unlikely. Developmental evaluation, which is more conceptually aligned with service-design thinking, moves beyond these assumptions, emphasizing supportive adaptation to ensure meaningful adoption. Research limitations/implications Blended approaches that incorporate service-design thinking and rely more heavily on developmental strategies are essential for complex interventions. To maximize the benefit of this approach, three guiding principles are suggested: stress pragmatism over stringency; adopt an implementation lens; and use multi-disciplinary teams to run studies. Originality/value This viewpoint offers novel thinking on the debate around appropriate evaluation methodologies to be applied to complex interventions like models of integrated care.
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 | 0.141 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".