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Record W4391615674 · doi:10.1186/s40900-024-00551-9

Lessons learned in measuring patient engagement in a Canada-wide childhood disability network

2024· article· en· W4391615674 on OpenAlexafffundabout
Tatiana Ogourtsova, Miriam González, Alix Zerbo, Frank Gavin, Keiko Shikako‐Thomas, Jonathan A. Weiss, Annette Majnemer

Bibliographic record

VenueResearch Involvement and Engagement · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsYork UniversityMontreal Children's HospitalUniversity of ManitobaJewish Rehabilitation HospitalMcGill UniversityCentre for Interdisciplinary Research in RehabilitationCentre Integre de Sante et de Services Sociaux de LavalMcGill University Health Centre
FundersCanadian Institutes of Health ResearchCentre for Interdisciplinary Research in Rehabilitation
KeywordsGeneral partnershipCommunity-based participatory researchThematic analysisParticipatory action researchCommunity engagementPublic engagementPatient experiencePsychologyPatient satisfactionDescriptive statisticsPatient participationNursingMedical educationMedicineMEDLINEQualitative researchHealth carePublic relationsBusinessPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The CHILD-BRIGHT Network, a pan-Canadian childhood disability research Network, is dedicated to patient-oriented research, where numerous stakeholders, including patient-partners, researchers, and clinicians are involved at different levels. The Network is committed to continuously improving the level of engagement and partnerships' impact. Measuring patient engagement is therefore important in reflecting on our practices and enhancing our approaches. We aimed to measure patient engagement longitudinally and explore in greater depth the perceived benefits, barriers and facilitators, and overall satisfaction with patient engagement, from the perspectives of the different stakeholders. METHODS: Patient engagement was measured using online surveys. In a longitudinal study design over a 3-years period (2018-2020) the Community-Based Participatory Research (CBPR) questionnaire was used. To enrich our understanding of patient engagement in Year 3, we employed the Public and Patient Engagement Evaluation Tool (PPEET) in a cross-sectional, convergent parallel mixed-method study design. Descriptive statistics and a thematic-based approach were used for data analysis. RESULTS: The CBPR questionnaire was completed by n = 167 (61.4% response rate), n = 92 (30.2% response rate), and n = 62 (14.2% response rate) Network members in Years 1, 2, and 3, respectively. Ninety-five (n = 95, 21.8% response rate) members completed the PPEET in Year 3. CBPR findings demonstrate a stable and high satisfaction level with patient engagement over time, where 94%, 86%, and 94% of stakeholders indicated that the project is a "true partnership" in Years 1, 2, and 3, respectively. In Years 2 and 3, we noted an improvement in patient-partners' comfort level in sharing their views and perspectives (92% and 91% vs. 74%). An increase in critical reflective trust (i.e., allowing for discussing and resolving mistakes) from Year 1 to 3 was found, both from the perspectives of patient-partners (51-65%) and researchers (48-75%). Using the PPEET, patient engagement factors (i.e., communications and supports for participation, ability to share views and perspectives) and impact were highly rated by most (80-100%) respondents. PPEET's qualitative responses revealed several patient engagement advantages (e.g., increased projects' relevance, enhanced knowledge translation), barriers (e.g., group homogeneity), facilitators (e.g., optimal communication strategies), and solutions to further improve patient engagement (e.g., provide clarity on goals). CONCLUSION: Our 3-years patient engagement evaluation journey demonstrated a consistent and high level of satisfaction with patient engagement within the Network and identified advantages, barriers, facilitators, and potential solutions. Improvements were observed in members' comfort in sharing their views and perspectives, along with an increase in critical reflective trust. These findings underscore the Network's commitment to enhancing patient engagement and provide valuable insights for continued improvement and optimization of collaborative efforts.

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)0.019
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.558
GPT teacher head0.487
Teacher spread0.071 · 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

Citations10
Published2024
Admission routes3
Has abstractyes

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