Reporting GUideline for Intervention DEscription in Rehabilitation (GUIDE-Rehab): a tool to open the ‘black box’ of rehabilitation complex interventions
Bibliographic record
Abstract
In 2023, the World Health Assembly adopted a resolution to strengthen rehabilitation within health systems, calling for rehabilitation research. Within health, the term rehabilitation has multiple meanings, including a core strategy, a sector, a service and an intervention. The latter has been defined as complex and characterised as a 'black box', similar to complex interventions in other fields. The existing reporting guidelines are not sufficiently effective in describing interventions within the rehabilitation field. We developed the GUideline for Intervention DEscription in Rehabilitation (GUIDE-Rehab) to address these challenges.According to the Enhancing the QUAlity and Transparency Of health Research Network, we followed a Delphi process with multiple Consensus Meetings and piloting and used ACcurate COnsensus Reporting Document for reporting. The background research involved 21 papers. We based GUIDE-Rehab on the Rehabilitation Treatment Specification System, developed over 15 years of research to improve rehabilitation description; the definition of rehabilitation for research purposes; and the Template for Intervention Description and Replication reporting guideline. 68 representatives from global rehabilitation stakeholders (scientific societies, journals, evidence and methods groups), including individuals with lived experience of disability, from 26 countries across all continents and economies, participated. The piloting involved 17 chief editors, 7 research groups and participants from 10 scientific meetings.The complete version comprises 16 items, while the version for uncontrolled studies includes 13. The short version (10 items for text, 6 for appendix) helps reduce the manuscripts' length. The GUIDE-Rehab graphical illustration (nine items) facilitates the intervention description. GUIDE-Rehab will assist in the reporting of interventions in rehabilitation to enhance clinical research and support clinical implementation.
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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 | 0.293 | 0.423 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".