Preferences of Knowledge Users for Two Formats of Summarizing Results from Systematic Reviews: Infographics and Critical Appraisals
Bibliographic record
Abstract
OBJECTIVES: To examine and compare preferences of knowledge users for two different formats of summarizing results from systematic reviews: infographics and critical appraisals. DESIGN: Cross-sectional. SETTING: Annual members' meeting of a Network of Centres of Excellence in Knowledge Mobilization called TREKK (Translating Emergency Knowledge for Kids). TREKK is a national network of researchers, clinicians, health consumers, and relevant organizations with the goal of mobilizing knowledge to improve emergency care for children. PARTICIPANTS: Members of the TREKK Network attending the annual meeting in October 2013. OUTCOME MEASURES: Overall preference for infographic vs. critical appraisal format. Members' rating of each format on a 10-point Likert scale for clarity, comprehensibility, and aesthetic appeal. Members' impressions of the appropriateness of the two formats for their professional role and for other audiences. RESULTS: Among 64 attendees, 58 members provided feedback (91%). Overall, their preferred format was divided with 24/47 (51%) preferring the infographic to the critical appraisal. Preference varied by professional role, with 15/22 (68%) of physicians preferring the critical appraisal and 8/12 (67%) of nurses preferring the infographic. The critical appraisal was rated higher for clarity (mean 7.8 vs. 7.0; p = 0.03), while the infographic was rated higher for aesthetic appeal (mean 7.2 vs. 5.0; p<0.001). There was no difference between formats for comprehensibility (mean 7.6 critical appraisal vs. 7.1 infographic; p = 0.09). Respondents indicated the infographic would be most useful for patients and their caregivers, while the critical appraisal would be most useful for their professional roles. CONCLUSIONS: Infographics are considered more aesthetically appealing for summarizing evidence; however, critical appraisal formats are considered clearer and more comprehensible. Our findings show differences in terms of audience-specific preferences for presentation of research results. This study supports other research indicating that tools for knowledge dissemination and translation need to be targeted to specific end users' preferences and needs.
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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 | 0.193 | 0.522 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".