Enhancing the uptake of systematic reviews of effects: what is the best format for health care managers and policy-makers? A mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are infrequently used by health care managers (HCMs) and policy-makers (PMs) in decision-making. HCMs and PMs co-developed and tested novel systematic review of effects formats to increase their use. METHODS: A three-phased approach was used to evaluate the determinants to uptake of systematic reviews of effects and the usability of an innovative and a traditional systematic review of effects format. In phase 1, survey and interviews were conducted with HCMs and PMs in four Canadian provinces to determine perceptions of a traditional systematic review format. In phase 2, systematic review format prototypes were created by HCMs and PMs via Conceptboard©. In phase 3, prototypes underwent usability testing by HCMs and PMs. RESULTS: Two hundred two participants (80 HCMs, 122 PMs) completed the phase 1 survey. Respondents reported that inadequate format (Mdn = 4; IQR = 4; range = 1-7) and content (Mdn = 4; IQR = 3; range = 1-7) influenced their use of systematic reviews. Most respondents (76%; n = 136/180) reported they would be more likely to use systematic reviews if the format was modified. Findings from 11 interviews (5 HCMs, 6 PMs) revealed that participants preferred systematic reviews of effects that were easy to access and read and provided more information on intervention effectiveness and less information on review methodology. The mean System Usability Scale (SUS) score was 55.7 (standard deviation [SD] 17.2) for the traditional format; a SUS score < 68 is below average usability. In phase 2, 14 HCMs and 20 PMs co-created prototypes, one for HCMs and one for PMs. HCMs preferred a traditional information order (i.e., methods, study flow diagram, forest plots) whereas PMs preferred an alternative order (i.e., background and key messages on one page; methods and limitations on another). In phase 3, the prototypes underwent usability testing with 5 HCMs and 7 PMs, 11 out of 12 participants co-created the prototypes (mean SUS score 86 [SD 9.3]). CONCLUSIONS: HCMs and PMs co-created prototypes for systematic review of effects formats based on their needs. The prototypes will be compared to a traditional format in a randomized trial.
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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 | 0.628 | 0.733 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".