An iterative evaluation of two shortened systematic review formats for clinicians: a focus group study
Bibliographic record
Abstract
OBJECTIVE: To conduct a series of focus groups with primary care physicians to determine the optimal format of a shortened, focused systematic review. MATERIALS AND METHODS: Prototypes for two formats of a shortened systematic review were developed and presented to participants during focus group sessions. Focus groups were conducted with primary care physicians who were in full- or part-time practice. An iterative process was used so that the information learned from the first set of focus groups (Round 1) influenced the material presented to the second set of focus groups (Round 2). The focus group discussions were recorded, transcribed verbatim, and analyzed. RESULTS: Each of the two rounds of testing included three focus groups. A total of 32 physicians participated (Round 1:16 participants; Round 2:16 participants). Analysis of the transcripts from Round 1 identified three themes including ease of use, clarity, and implementation. Changes were made to the prototypes based on the results so that the revised prototypes could be presented and discussed in the second round of focus groups. After analysis of transcripts from Round 2, four themes were identified, including ease of use, clarity, brevity, and implementation. Revisions were made to the prototypes based on the results. CONCLUSIONS: Primary care physicians provided input on the refinement of two prototypes of a shortened systematic review for clinicians. Their feedback guided changes to the format, presentation, and layout of these prototypes in order to increase usability and uptake for end-users.
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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 | 0.638 | 0.795 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".