Assessing the format and content of journal published and non-journal published rapid review reports: A comparative study
Bibliographic record
Abstract
BACKGROUND: As production of rapid reviews (RRs) increases in healthcare, knowing how to efficiently convey RR evidence to various end-users is important given they are often intended to directly inform decision-making. Little is known about how often RRs are produced in the published or unpublished domains, and what and how information is structured. OBJECTIVES: To compare and contrast report format and content features of journal-published (JP) and non-journal published (NJP) RRs. METHODS: JP RRs were identified from key databases, and NJP RRs were identified from a grey literature search of 148 RR producing organizations and were sampled proportionate to cluster size by organization and product type to match the JP RR group. We extracted and formally compared 'how' (i.e., visual arrangement) and 'what' information was presented. RESULTS: We identified 103 RRs (52 JP and 51 NJP) from 2016. A higher percentage of certain features were observed in JP RRs compared to NJP RRs (e.g., reporting authors; use of a traditional journal article structure; section headers including abstract, methods, discussion, conclusions, acknowledgments, conflict of interests, and author contributions; and use of figures (e.g., Study Flow Diagram) in the main document). For NJP RRs, a higher percentage of features were observed (e.g., use non-traditional report structures; bannering of executive summary sections and appendices; use of typographic cues; and including outcome tables). NJP RRs were more than double in length versus JP RRs. Including key messages was uncommon in both groups. CONCLUSIONS: This comparative study highlights differences between JP and NJP RRs. Both groups may benefit from better use of plain language, and more clear and concise design. Alternative innovative formats and end-user preferences for content and layout should be studied further with thought given to other considerations to ensure better packaging of RR results to facilitate uptake into policy and practice. STUDY REGISTRATION: The full protocol is available at: https://osf.io/29xvk/.
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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 | 0.323 | 0.795 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".