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Record W4387636963 · doi:10.1186/s40900-023-00486-7

Encouraging diversity in family engagement in research: Reflections on the development of knowledge translation tools

2023· article· en· W4387636963 on OpenAlexafffundabout
Janet W. T. Mah, Katie Nickerson

Bibliographic record

VenueResearch Involvement and Engagement · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersKids Brain Health Network
KeywordsInfographicGeneral partnershipDiversity (politics)Public relationsPsychologyMedical educationMedicineSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Family engagement in research is crucial to generating relevant, impactful, and meaningful priorities and outcomes. Although there has been increased awareness and value for patient-oriented research, most patient partners in North America are from Western, educated, industrialized, rich and democratic societies. Encouraging underserviced and marginalized populations to join the partnerships is important. This project demonstrates the development of two knowledge translation tools created to encourage diversity in patient-family and researcher partnerships. CASE STUDY: Our diverse cross-Canadian team embodies the family-researcher partnership as it consists of two research personnel from non-Western origins with immigrant experiences, a parent with lived experience, and a project director. All group members have experience in the field of mental health and neurodevelopmental conditions. Four infographics were created: 3 patient-oriented ones (in English, Chinese, and Farsi) and 1 researcher-targeted one. Content for the infographics were generated to address common barriers to patient engagement identified from literature reviews, as well as key concepts discussed during the McMaster University Continuing Education Family Engagement in Research Certificate Course sponsored by CanChild & Kids Brain Health Network. Peer consultations helped to improve the infographics to be more culturally sensitive and appealing. The patient-oriented infographic presents concise bullet points about 5 main topics: (1) what is research, (2) reasons to join, (3) your role, (4) talking to researchers, and (5) how to join. The researcher-targeted infographic presents concise bullet points about 4 topics: 1) why team up with diverse patient partners, (2) ways to partner, (3) how to connect, and (4) talking to diverse partners. CONCLUSION: Infographics were co-designed to encourage diversity in family engagement in research. Lessons learned throughout the project include barriers encountered (e.g., team collaboration considerations, design limitations) and strategies that facilitated the project (e.g., online collaboration platforms). Future directions include translations into other languages, increased dissemination across agencies, and evaluating the effectiveness of the infographic tools.

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)0.063
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0630.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.959
GPT teacher head0.630
Teacher spread0.330 · 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.

Study designQualitative
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

Citations3
Published2023
Admission routes3
Has abstractyes

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