Determining the Impact of a Physiotherapist-Led Primary Care Model for Low Back Pain: Protocol and Analysis Plan for a Cluster Randomized Controlled Trial and Embedded Process Evaluation (Preprint)
Notice bibliographique
Résumé
BACKGROUND Low back pain (LBP) is a common and disabling condition that is costly for health systems and society. Interprofessional primary care models may improve care quality and reduce this burden. OBJECTIVE This protocol and analysis plan communicates the methods for a cluster randomized trial with the following objectives: (1) evaluate the effectiveness of a physiotherapist-led (PT-led) primary care model for LBP at improving disability (primary outcome), pain intensity, quality of life, global rating of change, patient satisfaction, and adverse events compared with usual physician-led primary care; and (2) determine the impact of the PT-led primary care model for LBP on the health care system and society (health care access, health care use, missed work, cost-effectiveness). Both objectives are evaluated over a 1-year period. A multimethod process evaluation is embedded to assess model implementation, mechanisms, perspectives of patients and providers, and contextual influences. METHODS This study is a cluster randomized controlled trial with 20 primary care practices (clusters) in Canada, randomized 1:1 to a PT-led or usual physician-led primary care model for LBP. Adults seeking care from their primary care team for LBP are recruited over 1 year. Data collection occurs at baseline, 6 weeks, and 3, 6, 9, and 12 months. Effectiveness will be analyzed using linear mixed regression. The process evaluation analysis will include: descriptive and comparative analyses to assess implementation; descriptive and mediation analyses to assess potential mechanisms; qualitative interpretive description to understand experiences and perspectives of patients, PTs, and other health professionals; and mixed methods to determine contextual influences on implementation. RESULTS Recruitment of primary care sites (clusters) was completed in June 2023, following delays related to the COVID-19 pandemic. Cluster randomization occurred in July 2023. Recruitment of patient participants began in October 2023 and concluded in November 2024 (n=739). The final self-reported patient data was collected on November 25, 2025. Extraction of electronic health record data is scheduled for completion on December 19, 2025. Data analysis will be conducted in accordance with the study protocol and analysis plan and will begin once all data collection activities are complete. No interim analyses have been performed. CONCLUSIONS The results of this trial will provide evidence for knowledge users to determine whether a PT-led primary care model for LBP is effective and should be adopted more widely. Knowledge users have identified the impact of the new model of care on disability, quality of life, and cost-effectiveness as key evidence needed to inform key decision-making. The multimethod process evaluation will provide critical evidence to interpret trial results and inform future scale and spread of this model of care if effective. CLINICALTRIAL ClinicalTrials.gov NCT04287413; https://clinicaltrials.gov/study/NCT04287413 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/89004
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 | 0,123 | 0,148 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,008 | 0,012 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,010 |
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 ».