Knowledge mobilization activities to support decision-making by youth, parents, and adults using a systematic and living map of evidence and recommendations on COVID-19: protocol for three randomized controlled trials and qualitative user-experience studies
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic underlined that guidelines and recommendations must be made more accessible and more understandable to the general public to improve health outcomes. The objective of this study is to evaluate, quantify, and compare the public’s understanding, usability, satisfaction, intention to implement, and preference for different ways of presenting COVID-19 health recommendations derived from the COVID-19 Living Map of Recommendations and Gateway to Contextualization (RecMap). Methods and analysis This is a protocol for a multi-method study. Through an online survey, we will conduct pragmatic allocation-concealed, blinded superiority randomized controlled trials (RCTs) in three populations to test alternative formats of presenting health recommendations: adults, parents, and youth, with at least 240 participants in each population. Prior to initiating the RCT, our interventions will have been refined with relevant stakeholder input. The intervention arm will receive a plain language recommendation (PLR) format while the control arm will receive the corresponding original recommendation format as originally published by the guideline organizations (standard language version). Our primary outcome is understanding, and our secondary outcomes are accessibility and usability, satisfaction, intended behavior, and preference for the recommendation formats. Each population’s results will be analyzed separately. However, we are planning a meta-analysis of the results across populations. At the end of each survey, participants will be invited to participate in an optional one-on-one, virtual semi-structured interview to explore their user experience. All interviews will be transcribed and analyzed using the principles of thematic analysis and a hybrid inductive and deductive approach. Ethics and dissemination Through Clinical Trials Ontario, the Hamilton Integrated Research Ethics Board has reviewed and approved this protocol (Project ID: 3856). The University of Alberta has approved the parent portion of the trial (Project ID:00114894). Findings from this study will be disseminated through open-access publications in peer-reviewed journals and using social media. Trial registration Clinicaltrials.gov NCT05358990 . Registered on May 3, 2022
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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 | 0.109 | 0.123 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".