Lessons learned in measuring patient engagement in a Canada-wide childhood disability network
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,117 | 0,140 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,009 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,004 | 0,009 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».