Towards More Appropriate Care for Low Back Pain: Understanding Primary Care Factors that Increase Best Practices and Collaborative Approaches to Care
Notice bibliographique
Résumé
Moving towards more appropriate care for low back pain is an important health services research issue in Ontario, Canada especially as evidence suggests there is overuse of health care resources for this problem. Low back pain involves excessive imaging, over-referrals for specialist care, and overuse of drug therapy. At the present time, there is also poor uptake of best practices, an evidence to practice gap, and low back pain management and outcomes have changed little over the past 35 years. The emerging field of implementation science will be useful to address the central research question which asks “What factors amongst primary care physicians that manage low back pain, enable or constrain their ability to implement best practices in their care?“ Implementation science (IS) is drawn upon to help advance the uptake of research findings into practice and provide understanding as to why the implementation of evidence into practice may be successful or not. Using the Consolidated Framework for Implementation Research (CFIR) model helps to identify barriers and facilitators to the uptake of best practices. Older frameworks have focused narrowly on physician behavior change, but CFIR recognizes that implementation must also consider features of context and setting to have a greater chance of success. As such, CFIR informs the study by using a meta-analytic framework, and focuses on elements of intervention characteristics, outer setting, inner setting, individual characteristics and process, which have been found to influence uptake of interventions. The study uses a mixed methods approach which is appropriate when attempting to understand the social and practice environment involved in providing primary care. In this multi-phase study, a literature review was first conducted to provide a current understanding of what is currently known on the topic. This informed Phase 2, which involved sending quantitative surveys to a sample of Ontario primary care providers, mainly including family physicians. A final Phase 3 involved qualitative in-depth interviews with a subset of those providers to provide context and help explain patterns in the research findings. Results from 240 surveys will be presented, including a discussion of descriptive statistics, inferential statistics, hypothesis testing and a principal components analysis (PCA). Findings from 21 interviews will also be discussed, where 7 themes and multiple subthemes were found, with a particular emphasis on: the usage of evidence, inter-professional practice, changing practice patterns, expanding virtual care, and centering patient preferences. Overall, multiple barriers and facilitators for the adoption of best practices including the use of clinical practice guidelines will be discussed. The research committee, engaged in the project co-design, is made up of an interdisciplinary team of health service researchers, clinicians and academics with diverse expertise and experience. The study aims to address gaps in the application of best practices for providers delivering low back pain care and recommends more integrated models of care. An overall goal is to help improve low back pain management at the primary care level, as this remains a prevalent and costly issue for the Canadian province of Ontario.
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 | 0,074 | 0,190 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,007 | 0,008 |
| Communication savante | 0,017 | 0,014 |
| Science ouverte | 0,004 | 0,010 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».