Notice bibliographique
Résumé
There is a proliferation of clinical practice guidelines (CPGs) in medicine and rehabilitation. The purpose of a CPG is to distil research evidence into recommendations for clinical decision making.2 Cutforth et al.'s description1 of the development of the Alberta CPG for low back pain (LBP) is unique and exciting for three main reasons: it was developed collaboratively, is contextually relevant, and is multidisciplinary in nature. (Please also view their video at http://www.youtube.com/watch?v=lkPv72O9ums.) The principle behind evidence-based practice (EBP) is that health care practitioners should use available evidence to determine effective, patient-relevant treatment. Yet study after study has revealed a significant gap between what is known and what is practised, that is, a gap between evidence and practice. The good news is that the gap can be partially bridged by translating evidence into CPGs. Regrettably, however, the uptake of guidelines in Canada has been less than successful.3 For example, knowledge about best practices for subacute and chronic LBP remains low.4,5 Harman et al.6 found that although physiotherapists understand the importance of active versus passive treatment in managing subacute LBP, they continue to use passive interventions that are not supported by the literature. Thus, the remaining gap requires a bridge in the form of actual acceptance and use of guidelines in everyday clinical practice (i.e., guideline uptake).5 The characteristics of the guideline itself, of the practitioner/professional, and the of practice setting are factors that affect guideline uptake.3,7–9 The Alberta researcher–clinician partnership process described by Cutforth et al. confronts this issue and attempts to diminish the barriers to guideline uptake. Here, front-line clinicians worked with researchers to bridge the gap between research findings and service delivery, taking into consideration the health care environment in Alberta. Their process and methodology represent a model for others who aim to improve outcomes by integrating existing CPGs into practice in the management of LBP. This guideline incorporates existing strong evidence for the multidimensional bio-psychosocial approach to pain assessment and management. Clinicians working in a collaborative inter-professional environment will find these guidelines immediately useful. It is clear that one-dimensional treatment interventions alone have only modest success in preventing or remediating chronic pain and related disability. For example, there is insufficient evidence for the effectiveness of any single intervention (e.g., back schools, low-level laser therapy, patient education, massage, traction, superficial heat/cold, or lumbar supports) for chronic LBP.10 Pharmacological management alone is equally disappointing in managing chronic pain. Yet there is moderate to strong evidence that multidimensional, inter-professional bio-psychosocial approaches that include self-management strategies provide the best outcomes.11–17,18–21 It is time to implement this evidence in practice. Barriers to guideline uptake in one practice setting may not be present in another.9 Inter-professional guidelines such as those developed in Alberta require collaborative practice.22 In Alberta, physical therapists are integrated into the team and play a critical role in collaborative, inter-professional bio-psychosocial assessment and management of chronic pain. In some other provinces, however, this is not the case. For example, the Ontario Ministry of Health and Long-Term Care has developed Family Health Teams (FHTs) comprising nurses, nurse practitioners, dieticians, social workers, psychologists, physicians, and pharmacists but, to date, has refused to fund physiotherapists as part of FHTs. Therefore, FHTs must rely on other professions to implement evidence-based care. This means that in the Ontario public sector, primary-care physical therapy is considered by government to be complementary rather than essential. Collaborative practice for the prevention and remediation of chronic disease is one of our main strengths as a profession. Rehabilitation for individuals with other chronic diseases, such as stroke, diabetes, chronic lung disease, and osteoporosis, has long been inter-professional and based on patient-centred functional goals. In assessing and managing these conditions, physiotherapists are essential, and they successfully use a chronic-disease rehabilitation model to improve care and outcomes. These are examples not just of an acute model applied to a chronic condition but, instead, of an evidence-based model of care. The same collaborative approach is required for the management of chronic pain. Interventions such as personalized goal setting, graded movement, self-management, work readiness, and functional restoration are essential components23 of rehabilitation for chronic pain. Cutforth et al.1 have provided a model by developing a multidisciplinary CPG that integrates knowledge into collaborative practice for multiple health professions. This is a logical and meaningful first step. Let's embrace it!
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 | 0,009 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,009 | 0,005 |
| Intégrité de la recherche | 0,068 | 0,061 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,028 |
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 ».