Experiences of physiotherapists working with adults living with Long COVID in Canada: a qualitative study
Notice bibliographique
Résumé
ABSTRACT Objectives To explore experiences of physiotherapists working with adults living with Long COVID in Canada. Design Cross-sectional descriptive qualitative study involving online semi-structured interviews. Participants We recruited physiotherapists in Canada who self-identified as having clinically treated one or more adults living with Long COVID in the past year. Data collection Using an interview guide, we inquired about physiotherapists’ knowledge of Long COVID, assessment and treatment experiences, perspectives on physiotherapists’ roles, contextual and implementation factors influencing rehabilitative outcomes, and their recommendations for Long COVID rehabilitation. Interviews were audio-recorded, transcribed verbatim, and analyzed using a group-based thematic analytical approach. We administered a demographic questionnaire to describe sample characteristics. Results Thirteen physiotherapists from five provinces participated; most were women (n=8;62%) and practised in urban settings (n=11;85%). Participants reported variable amounts of knowledge of existing guidelines and experiences working with adults living with Long COVID in the past year. Physiotherapists characterized their experiences working with adults living with Long COVID as a dynamic process involving: 1) a disruption to the profession (encountering a new patient population and pivoting to new models of care delivery), followed by 2) a cyclical process of learning curves and evolving roles of physiotherapists working with persons living with Long COVID (navigating uncertainty, keeping up with rapidly-emerging evidence, trial and error, adapting mindset and rehabilitative approaches, and growing prominence of roles as advocate and collaborator). Participants recommended the need for education and training, active and open-minded listening with patients, interdisciplinary models of care, and organizational- and system-level improvements to foster access to care. Conclusions Physiotherapists’ experiences involved a disruption to the profession followed by a dynamic process of learning curves and evolving roles in Long COVID rehabilitation. Not all participants demonstrated an in-depth understanding of existing Long COVID rehabilitation guidelines. Results may help inform physiotherapy education in Long COVID rehabilitation. STRENGTHS AND LIMITATIONS OF THIS STUDY To our knowledge, this is one of the first qualitative studies to explore experiences of physiotherapists working with patients living with Long COVID in Canada. Our qualitative approach, involving online semi-structured interviews, enabled an in-depth exploration into Canadian physiotherapists’ perceived roles in Long COVID care, experiences with assessment and treatment, knowledge acquisition, and facilitators and barriers to delivery of rehabilitation services. Our team-based analytical approach and partnership with physiotherapists living with Long COVID as part of our process provided valuable collaboration, guidance, and advice for refining the interview guide and demographic questionnaire and fostering student researcher interview skills to increase the quality of the study. The diversity of participants’ characteristics working in different practice settings across five Canadian provinces and the variability in the number of individuals with Long COVID treated by participants were strengths of the study. However, as most participants practised in urban settings in Canada, transferability to other geographical contexts including rural settings and other countries may be limited, especially those with larger differences in healthcare systems.
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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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,023 | 0,011 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».