A Training Program to Support Patient Engagement in Primary Health Care Research: Co-Design, Implementation, and Evaluation Study
Notice bibliographique
Résumé
Background: Patient engagement in research represents an evolution in how new knowledge is being created. Individuals and teams seeking to conduct research in this way want to learn how to best approach this aspect. Specialized training is required to ensure that these individuals and groups have the knowledge and skills to engage with and accomplish these goals. We developed a training program, called Patient-Oriented Research Training & Learning - Primary Health Care (PORTL-PHC), to address this need. Objective: The objective of this paper was to describe key learning needs and knowledge gaps regarding patient-oriented research in primary health care, as well as the design, implementation, and evaluation of the PORTL-PHC program. Methods: First, we completed a needs assessment to determine the learning needs of the program's target groups (including patient partners, policy makers, health care practitioners, and researchers). Second, building on the results of the needs assessment, the development and implementation of the program followed a series of iterative steps, including user testing of the program's content and format. Third, we conducted an evaluation with two components: (1) program registrants were asked to respond to questions as they progressed through the training content that explored what aspects of the content users found the most useful, suggestions for improvement, and any difficulties navigating the learning platform; and (2) program registrants were administered a questionnaire in three waves (January 2020, July 2020, and September 2021) 6 months after they had completed the program, that asked them to rate their gains in different areas of knowledge and skills regarding patient-oriented research on a 5-point Likert scale. Results: There were 205 learners who participated in the program from January 2018 to January 2022. The target audience was reached with registrants from all groups; the majority of learners were from Canada (194/205, 95%). A total of 6 main areas of knowledge needs were identified from the needs assessment, and the program was iteratively developed and refined to address these needs and our learning objectives. Suggestions for improvement received from the first component of the evaluation were used to enhance and refine the program. Of the 88 learners who had completed the program at the time of the evaluation questionnaire administration, 28 responded to our request to complete an evaluation. The results indicate that PORTL-PHC increased knowledge of patient-oriented PHC research (overall mean score of 4.36, SD .56). Learners gained skills and knowledge in identifying patient priorities in PHC (mean 4.27, SD .63), understanding the methods of patient engagement (mean 4.32, SD .65), and skills for engagement in patient-oriented research (mean 4.41, SD .50). The majority of respondents (23/28, 82%) indicated that they intended to use the information from the PORTL-PHC training program in the future. Conclusions: Through the PORTL-PHC program, we are training a new cadre of interested individuals who are committed to patient engagement in research to improve the provision of primary health care, and thus, patient outcomes.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».