Development of a checklist to assess the quality of reporting of knowledge translation interventions using the Workgroup for Intervention Development and Evaluation Research (WIDER) recommendations
Bibliographic record
Abstract
BACKGROUND: Influenced by an important paper by Michie et al., outlining the rationale and requirements for detailed reporting of behavior change interventions now required by Implementation Science, we created and refined a checklist to operationalize the Workgroup for Intervention Development and Evaluation Research (WIDER) recommendations in systematic reviews. The WIDER recommendations provide a framework to identify and provide detailed reporting of the essential components of behavior change interventions in order to facilitate replication, further development, and scale-up of the interventions. FINDINGS: The checklist was developed, applied, and improved over the course of four systematic reviews of knowledge translation (KT) strategies in a variety of healthcare settings conducted by Scott and associates. The checklist was created as one method of operationalizing the work of the WIDER in order to facilitate comparison across heterogeneous studies included in these systematic reviews. Numerous challenges were encountered in the process of creating and applying the checklist across four stages of development. The resulting improvements have produced a 'user-friendly' and replicable checklist to assess the quality of reporting of KT interventions in systematic reviews using the WIDER recommendations. CONCLUSIONS: With journals, such as Implementation Science, using the WIDER recommendations as publication requirements for evaluation reports of behavior change intervention studies, it is crucial to find methods of examining, measuring, and reporting the quality of reporting. This checklist is one approach to operationalize the WIDER recommendations in systematic review methodology.
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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: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.589 | 0.746 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.044 | 0.021 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".