A multimethods randomized trial found that plain language versions improved parents’ understanding of health recommendations
Bibliographic record
Abstract
OBJECTIVES: To assess the effectiveness of plain language compared with standard language versions of COVID-19 recommendations specific to child health. STUDY DESIGN AND SETTING: Pragmatic, allocation-concealed, blinded, superiority randomized controlled trial with nested qualitative component. Trial was conducted online, internationally. Parents or legal guardians (≥18 years) of a child (<18 years) were eligible. Participants were randomized to receive a plain language recommendation (PLR) or standard (SLV) verison of a COVID-19 recommendation specific to child health. Primary outcome was understanding. Secondary outcomes included: preference, accessibility, usability, satisfaction, and intended behavior. Interviews explored perceptions and preferences for each format. RESULTS: Between July and August 2022, 295 parents were randomized; 241 (81.7%) completed the study (intervention n = 121, control n = 120). Mean understanding scores were significantly different between groups (PLR 3.96 [standard deviation (SD) 2.02], SLV 3.33 [SD 1.88], P = 0.014). Overall participants preferred the PLR version: mean rating 5.05/7.00 (95% CI 4.81, 5.29). Interviews (n = 12 parents) highlighted their preference for the PLR and provided insight on elements to enhance future knowledge mobilization of health recommendations. CONCLUSION: Compared to SLVs, parents preferred PLRs and better understood the recommendation. Guideline developers should strive to use plain language to increase understanding, uptake, and implementation of evidence by the public.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".