Standards for Reporting Implementation Studies (StaRI): explanation and elaboration document
Bibliographic record
Abstract
OBJECTIVES: Implementation studies are often poorly reported and indexed, reducing their potential to inform the provision of healthcare services. The Standards for Reporting Implementation Studies (StaRI) initiative aims to develop guidelines for transparent and accurate reporting of implementation studies. METHODS: An international working group developed the StaRI guideline informed by a systematic literature review and e-Delphi prioritisation exercise. Following a face-to-face meeting, the checklist was developed iteratively by email discussion and critical review by international experts. RESULTS: The 27 items of the checklist are applicable to the broad range of study designs employed in implementation science. A key concept is the dual strands, represented as 2 columns in the checklist, describing, on the one hand, the implementation strategy and, on the other, the clinical, healthcare or public health intervention being implemented. This explanation and elaboration document details each of the items, explains the rationale and provides examples of good reporting practice. CONCLUSIONS: Previously published reporting statements have been instrumental in improving reporting standards; adoption by journals and authors may achieve a similar improvement in the reporting of implementation strategies that will facilitate translation of effective interventions into routine practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.611 | 0.770 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.019 |
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".