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Enregistrement W4402307685 · doi:10.1186/s40900-024-00629-4

Enhancing patient-oriented research training: participant perceptions of an online course

2024· article· en· W4402307685 sur OpenAlexafffundabout
K Persson Wayne, Lillian MacNeill, Alison Luke, Grailing Anthonisen, Colleen McGavin, Linda Wilhelm, Shelley Doucet

Notice bibliographique

RevueResearch Involvement and Engagement · 2024
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensSpinal Cord Injury BCSaint John Regional HospitalCanadian Arthritis Patient AllianceUniversité de SherbrookeUniversity of New Brunswick
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésTraining (meteorology)Course (navigation)Medical educationPerceptionPsychologyParticipant observationKnowledge managementComputer scienceMedicineEngineeringSociology

Résumé

récupéré en direct d'OpenAlex

Patient-oriented research is now widely regarded as key to improving health systems and patient outcomes. This shift toward meaningful patient involvement in health research has sparked a growing interest in patient-oriented research training across Canada. Yet some barriers to participation, including distance and scheduling constraints, may impede the provision of in-person patient-oriented research training. Virtual course delivery options may help surmount those barriers, as well as offer unique pedagogical advantages. To help increase patient-oriented research training uptake, the research team adapted the Canadian Institutes of Health Research’s (CIHR) Strategy for Patient-Oriented Research’s Foundations for Patient-Oriented Research course to a virtual format. The course consists of three modules, which focus respectively on patient-oriented research, health research methods, and teamwork skills. The current evaluation of this virtual delivery examines how a diverse set of participants received the online course. Course participants from a variety of professional backgrounds, including researchers, patients, clinicians, and policy decision-makers, were recruited from across Canada to participate in the adapted course. Participant and facilitator feedback was solicited via online surveys that were distributed shortly after the delivery of each module. Over the span of the current project, the online course was delivered seven times across Canada. A total of 189 learners and 12 facilitators participated in the course. We received 89 completed feedback surveys in total. These included a total of 78 responses from learners, with 22 on Module 1, 32 on Module 2, and 24 on Module 3, in addition to 11 responses from facilitators. Overall, participants and facilitators were very satisfied with the course, indicating a successful adaptation from traditional to online delivery. Survey respondents were especially pleased with the course’s co-learning elements, which exposed them to fresh perspectives and real patient voices, as well as ample opportunity for discussion. Some participants offered recommendations for minor course revisions. Future iterations of the course will reflect participant and facilitator feedback to enhance accessibility via minor changes to course format (e.g., shorter live sessions), content (e.g., more concrete examples), and workload (e.g., reduced pre-work requirements). Sustainable and effective health care depends on health research that includes active partnerships across diverse populations. These collaborative relationships are fostered by strong capacity in patient-oriented research, which in turn hinges on widely accessible training opportunities. This online course overcomes common barriers to face-to-face training and offers the accessible, inclusive training environment required for sustained progress in patient-oriented research. In the past, patients were only involved in health research as study subjects and were excluded from membership on the research team. Today, it is the norm to involve patients and other non-researchers, such as clinicians and policy makers, as full, active partners in health research projects. This approach is called patient-oriented research, and is regarded as essential for good health care. In 2016, the Canadian Institutes of Health Research (CIHR) developed a course in patient-oriented research that helps people develop the skills they need to work together on a team with researchers, patients, caregivers, care providers, policy makers, and others. However, logistical challenges such as travel distance and scheduling conflicts may create barriers to in-person participation. Our research team adapted CIHR’s course in patient-oriented research for online delivery, which can help overcome these challenges and provide additional educational benefits. We delivered the online course seven times to diverse groups of participants from across Canada, including researchers, patients, clinicians, and policy makers. A total of 189 participants completed at least one of the three course modules. In this article, we examine the results of 89 completed feedback surveys (78 from learners and 11 from facilitators). Overall, the feedback was very positive, with participants appreciating the opportunity to learn from real patient experiences in an inclusive environment. We also received suggestions for improvement, such as reducing pre-work and using more concrete examples, which will be incorporated into future versions of the course. This evaluation shows that this course was successfully adapted for online delivery and offers a valuable opportunity for building skills in patient-oriented research.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,021
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,165
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0210,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,800
Tête enseignante GPT0,601
Écart entre enseignants0,200 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2024
Routes d'admission3
Résumé présentoui

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