Exploring the experiences of physiotherapists who engaged as knowledge users in integrated knowledge translation research partnerships related to balance measurement practices in Canadian hospitals: a qualitative descriptive study
Notice bibliographique
Résumé
Background: Integrated knowledge translation (IKT) is an approach to doing health research that engages academic researchers and knowledge users (KU) as equal partners. IKT intends to increase the chances that resulting research evidence will be useful to those engaged, striving toward improved health system functioning and public health outcomes. With this study, I set out to learn what physiotherapists (PTs) had to say about their experience engaging as KUs in an IKT research partnership related to balance measurement practices in Canadian hospitals. Methods: I used basic qualitative descriptive research methodology, in vivo coding, and conventional content analysis to answer the research questions. Five PTs (n=5) who had engaged as KUs on three balance measurement studies in two provinces were purposefully selected. All five (n=5) participated in online semi-structured interviews. PTs were asked to describe their IKT engagement experience, identify environmental factors that affected their engagement, and discuss how their engagement influenced the research process and evidence use. PTs also characterized themselves using an independently completed pre-interview questionnaire. Results: Participants described their experiences as positive, meaningful, and associated with benefits such as more clinical treatment options, greater sense of personal pride and professional recognition among PTs, increased research capacity for host organizations, and specific contributions to a body of knowledge. PTs said factors conducive to IKT engagement were supportive organizational culture, as well as devoted time, money, material resources, and human resources. PTs described their contributions to research as brokering trusting relationships; providing an insider point-of-view, project management, and resource coordination; and contributing to increased organizational capacity for research. Participants described how evidence-use was impacted by PT career-stage, individual risk perception, usefulness to the profession, organizational culture, treatment environment (especially since COVID-19 introduced pressures to deliver health care online), and third-party endorsement for change. Conclusions: KU engagement in IKT health research partnerships provides researchers with increased clinical access, an insider point-of-view, and stronger research evidence. KU engagement increases the accessibility of resulting research evidence, but sustaining desired outcomes is another issue. The KU engagement experience is greatly affected by organizational culture. KU engagement concepts in IKT research partnerships must include feasibility and resource planning, as well as strategies for organizational change and risk management. PTs described external factors such as professional endorsement as being stronger influences on evidence use outcomes than research engagement. The IKT approach may be strengthened if issues related to change, risk, and resources are addressed early and often throughout the partnership.
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 | 0,014 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,022 | 0,015 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».