Determining the Impact of a Physiotherapist-Led Primary Care Model for Hip and Knee Pain: Protocol and Analysis Plan for a Cluster Randomized Controlled Trial With Process Evaluation (Preprint)
Notice bibliographique
Résumé
BACKGROUND Hip and knee pain are leading contributors to disability, reduced quality of life, and health care burden in Canada. Primary care is often the first point of contact for patients with these conditions, yet timely and appropriate care is limited due to provider shortages and system pressures. Interest is growing in interprofessional primary care models that integrate physiotherapists to enhance care delivery for musculoskeletal conditions such as hip and knee pain. OBJECTIVE This study aims to communicate the protocol and analysis plan for a trial with two objectives: (1) to evaluate the effectiveness of a physiotherapist-led primary care model for hip and knee pain on physical functioning (primary outcome), pain intensity, quality of life, global rating of change, patient satisfaction, and adverse events, compared to usual physician-led primary care; and (2) to assess the impact of this model on health care system and societal outcomes, including access to care, health care use, productivity loss, and cost-effectiveness. A process evaluation will examine implementation, potential mechanisms, and patient experiences. METHODS A cluster randomized controlled trial involving 14 primary care organizations randomized equally to either a physiotherapist-led or usual physician-led primary care model for patients with hip and knee pain. Patients were recruited over 1 year, with data collected at baseline and at 3, 6, 9, and 12 months. The intervention integrates a physiotherapist as the initial point of contact within the primary care team for patients seeking care for hip or knee pain. It includes four components: (1) comprehensive assessment and screening, (2) brief individualized interventions during the initial visit, (3) guidance for accessing additional health care resources, and (4) follow-up physiotherapy for patients with unmet needs. Effectiveness will be assessed using linear mixed regression, accounting for clusters and prespecified covariates. The multimethods process evaluation will include descriptive and comparative analysis of implementation, mediation analysis to explore potential mechanisms, and qualitative exploration of patient experiences. RESULTS This research was funded in December 2022. Primary care sites (clusters) were recruited and randomized in June and July 2023, respectively. Patient enrollment occurred from October 2023 through November 2024. The final patient follow-up survey was completed in November 2025. Extraction of data from electronic health records is expected to finish in December 2025. Data analysis will begin after data collection is complete and will follow the predefined protocol and analysis plan. No interim analyses are planned. CONCLUSIONS Findings from this trial will provide actionable evidence on whether integrating physiotherapists into primary care teams for hip and knee pain improves patient outcomes and health care system efficiency. Effectiveness and process evaluation evidence will inform policymakers and health system leaders on the adoption and implementation of interprofessional, team-based primary care models. CLINICALTRIAL ClinicalTrials.gov NCT06358521; https://clinicaltrials.gov/study/NCT06358521 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/89006
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 | 0,090 | 0,105 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,004 |
| Méta-épidémiologie (sens large) | 0,012 | 0,013 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,049 | 0,009 |
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 ».