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Plain Language vs Standard Format for Youth Understanding of COVID-19 Recommendations

2023· article· en· W4385620125 on OpenAlexafffund
Adrian Sammy, Matthew Prebeg, Jacqueline Relihan, Ami Baba, Rana Charide, Shahab Sayfi, Lisa Hartling, Matthew Munan, Joseph L. Mathew, Tamara Kredo, Lawrence Mbuagbaw, Ashley Motilall, Shannon D. Scott, Miloslav Klugar, Tamara Lotfi, Adrienne L. Stevens, Kevin Pottie, Holger J. Schünemann, Nancy J. Butcher, Martin Offringa, Elie A. Akl, Jozef Suvada, Maicon Falavigna

Bibliographic record

VenueJAMA Pediatrics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity of AlbertaUniversity of TorontoCanadian Arthritis Patient AllianceWestern UniversityMcMaster UniversityHospital for Sick ChildrenMcMaster University Medical CentreCochraneCentre for Addiction and Mental HealthImpactPublic Health Agency of CanadaInstitute for Clinical Evaluative SciencesGlycemic Index LaboratoriesSickKids Foundation
FundersCanadian Institutes of Health Research
KeywordsMedicineUsabilityPreferenceMedical educationComprehensionPlain languageThe InternetMEDLINEDigital healthFamily medicineHealth careApplied psychologyPsychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Importance: To ensure that youths can make informed decisions about their health, it is important that health recommendations be presented for understanding by youths. Objective: To compare understanding, accessibility, usability, satisfaction, intention to implement, and preference of youths provided with a digital plain language recommendation (PLR) format vs the original standard language version (SLV) of a health recommendation. Design, Setting, and Participants: This pragmatic, allocation-concealed, blinded, superiority randomized clinical trial included individuals from any country who were 15 to 24 years of age, had internet access, and could read and understand English. The trial was conducted from May 27 to July 6, 2022, and included a qualitative component. Interventions: An online platform was used to randomize youths in a 1:1 ratio to an optimized digital PLR or SLV format of 1 of 2 health recommendations related to the COVID-19 vaccine; youth-friendly PLRs were developed in collaboration with youth partners and advisors. Main Outcomes and Measures: The primary outcome was understanding, measured as the proportion of correct responses to 7 comprehension questions. Secondary outcomes were accessibility, usability, satisfaction, preference, and intended behavior. After completion of the survey, participants indicated their interest in completing a 1-on-1 semistructured interview to reflect on their preferred digital format (PLR or SLV) and their outcome assessment survey response. Results: Of the 268 participants included in the final analysis, 137 were in the PLR group (48.4% female) and 131 were in the SLV group (53.4% female). Most participants (233 [86.9%]) were from North and South America. No significant difference was found in understanding scores between the PLR and SLV groups (mean difference, 5.2%; 95% CI, -1.2% to 11.6%; P = .11). Participants found the PLR to be more accessible and usable (mean difference, 0.34; 95% CI, 0.05-0.63) and satisfying (mean difference, 0.39; 95% CI, 0.06-0.73) and had a stronger preference toward the PLR (mean difference, 4.8; 95% CI, 4.5-5.1 [4.0 indicated a neutral response]) compared with the SLV. No significant difference was found in intended behavior (mean difference, 0.22 (95% CI, -0.20 to 0.74). Interviewees (n = 14) agreed that the PLR was easier to understand and generated constructive feedback to further improve the digital PLR. Conclusions and Relevance: In this randomized clinical trial, compared with the SLV, the PLR did not produce statistically significant findings in terms of understanding scores. Youths ranked it higher in terms of accessibility, usability, and satisfaction, suggesting that the PLR may be preferred for communicating health recommendations to youths. The interviews provided suggestions for further improving PLR formats. Trial Registration: ClinicalTrials.gov Identifier: NCT05358990.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.624
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.175
GPT teacher head0.484
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

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