Designing studies and reviews to produce informative, trustworthy evidence about complex interventions in rehabilitation: a narrative review and commentary
Bibliographic record
Abstract
According to Cochrane Rehabilitation's recently published definition for research purposes, rehabilitation is inherently complex. Rehabilitation teams frequently implement multiple strategies concurrently, draw on input from a range of different health professionals, target multiple outcomes, and personalize therapeutic plans. The success of rehabilitation lies not only in the specific therapies employed, but also in how they are delivered, when they are delivered, and the capability and willingness of patients to engage in them. In 2021, the UK Medical Research Council (MRC) and the National Institute of Health Research (NIHR) released the second major update of its framework for developing and evaluating complex interventions. This framework has direct relevance to the development and implementation of evidence-based practice in the field of rehabilitation. While previous iterations of this framework positioned complex interventions as anything that involved multiple components, multiple people, multiple settings, multiple targets of effect, and behavior change, this latest framework expanded on this concept of complexity to also include the characteristics and influence of the context in which interventions occur. The revised MRC-NIHR framework presents complex intervention research as comprising the following four inter-related and overlapping phases: 1) development or identification of the intervention; 2) feasibility; 3) evaluation; and 4) implementation, with different methods and tools required to address each of these phases. This paper provides an overview of the MRC-NIHR framework and its application to rehabilitation, with examples from past research. Rehabilitation researchers are encouraged to learn about the MRC-NIHR framework and its application. Funders of rehabilitation research are also encouraged to place greater emphasis on supporting studies that involve the right design to address key uncertainties in rehabilitation clinical practice. This will require investment into a broader range of types of research than simply individual-level randomized controlled trials. Rehabilitation research can both learn from and contribute to future iterations of the MRC-NIHR framework as it is an excellent environment for exploring complexity in clinical practice.
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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 | 0.085 | 0.366 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".