Improving reporting and utility of evaluations of complex interventions
Bibliographic record
Abstract
Global healthcare systems are buckling under the increasing burden of chronic disease and multimorbidity.1 Research into the efficacy of interventions to address chronic disease are needed. These potential solutions often include consideration of questions of complex, non-drug interventions such as processes of care, diet, exercise and behavioural interventions; areas with a deficit of study.2 Unfortunately, knowledge creation and dissemination is insufficient to affect behaviour, practice and policy change within diverse healthcare contexts.3 ,4 There is a need to study in situ—in multiple contexts—the impact of interventions. However, there have been concerns with the current reporting and quality of studies in these areas.2 ,5 In the design of studies on real-world interventions, researchers must move beyond unidirectional models of behaviour change and instead look to engaging evidence users, patients and clinicians, in: identifying questions and interventions of interest,6 and planning and executing the study to ensure that the implementation efforts are context appropriate.7–9 Co-creation in this way will increase the likelihood that the problems identified, and the solutions, resonate with the patients and clinicians; increasing the likelihood that there will be strong participant engagement and effort to implement and sustain the clinical change.8 ,9 From a researcher perspective there are significant challenges to undertaking work in this way. Non-medication interventions are of keen interest to patients and clinicians,6 but it is methodologically much easier to design a trial to evaluate a regulated intervention (medication, device and procedure), than it is to design and evaluate an assessment of non-regulated interventions (ie, service delivery, behavioural interventions, physical therapies).6 The significant up front engagement work of this approach is time consuming, challenging to achieve in research funding cycles, and high-risk because it requires contextual stability from real-world clinical care organisations to maintain …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.758 | 0.921 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".