Informing the development of the SUCCEED reporting guideline for studies on the scaling of health interventions: A systematic review
Bibliographic record
Abstract
BACKGROUND: Quality reporting contributes to effective translation of health research in practice and policy. As an initial step in the development of a reporting guideline for scaling, the Standards for reporting stUdies of sCaling evidenCEd-informED interventions (SUCCEED), we performed a systematic review to identify relevant guidelines and compile a list of potential items. METHODS: We conducted a systematic review according to Cochrane method guidelines. We searched the following databases: MEDLINE, Embase, PsycINFO, Cochrane Library, CINAHL, Web of Science, from their respective inceptions. We also searched websites of relevant organizations and Google. We included any document that provided instructions or recommendations, e.g., reporting guideline, checklist, guidance, framework, standard; could inform the design or reporting of scaling interventions; and related to the health sector. We extracted characteristics of the included guidelines and assessed their methodological quality using a 3-item internal validity assessment tool. We extracted all items from the guidelines and classified them according to the main sections of reporting guidelines (title, abstract, introduction, methods, results, discussion and other information). We performed a narrative synthesis based on descriptive statistics. RESULTS: Of 7704 records screened (published between 1999 and 2019), we included 39 guidelines, from which data were extracted from 57 reports. Of the 39 guidelines, 17 were for designing scaling interventions and 22 for reporting implementation interventions. At least one female author was listed in 31 guidelines, and 21 first authors were female. None of the authors belonged to the patient stakeholder group. Only one guideline clearly identified a patient as having participated in the consensus process. More than half the guidelines (56%) had been developed using an evidence-based process. In total, 750 items were extracted from the 39 guidelines and distributed into the 7 main sections. CONCLUSION: Relevant items identified could inform the development of a reporting guideline for scaling studies of evidence-based health interventions. This and our assessment of guidelines could contribute to better reporting in the science and practice of scaling.
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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 | 0.645 | 0.771 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.047 | 0.041 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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, 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".