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Record W4301186730 · doi:10.3138/ptc-2021-0057

Patient, Family, Caregiver, and Community Engagement in Research: A Sensibility Evaluation of a Novel Infographic and Planning Guide

2022· article· en· W4301186730 on OpenAlexaffvenue
Andrew Theodore Giannini, Megan Leong, Kelvin Chan, Arman Ghaltaei, Eden Graham, Craig Robinson, Malvina N. Skorska, Andrea Cross, Sharon Gabison

Bibliographic record

VenuePhysiotherapy Canada · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health NetworkCentre for Disability Prevention and RehabilitationCentre for Addiction and Mental HealthMcMaster University
Fundersnot available
KeywordsInfographicLikert scalePopulationPsychologySensibilityMedical educationApplied psychologyMedicineComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Background: Engaging patients, families, caregivers, and the community (PFCCs) throughout the research cycle ensures that research is meaningful for the target population. Although tools have been developed to promote PFCC engagement, many are lengthy, complex, and lack recommended behaviours. This study evaluated the sensibility of an infographic and accompanying planning guide for facilitating engagement of PFCCs in research. Methods: Thirteen rehabilitation researchers reviewed the PFCC engagement tool and planning guide, participated in a semi-structured interview, and completed a 10-item sensibility questionnaire. Interviews were transcribed, imported into NVivo, and analyzed using direct content analysis. Median scores and proportions of responses for each of the 10 items in the questionnaire were calculated. Results: Median scores for all questionnaire items were ≥ 4 on a 7-point Likert Scale. Participants reported the tool was easy to navigate, contained relevant items to promote PFCC engagement, and followed a logical sequence. Suggested modifications of the tool related to formatting, design, and changing the title. Conclusions: The tool was deemed sensible for overt format, purpose and framework, face and content validity, and ease of usage and provides guidance to engage PFCCs across the research cycle. Further studies are recommended to assess the effectiveness of the tool to engage PFCCs in research.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.637
GPT teacher head0.539
Teacher spread0.098 · 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 designObservational
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
Published2022
Admission routes2
Has abstractyes

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