Challenges in multiple sclerosis care: Results from an international mixed-methods study
Notice bibliographique
Résumé
BACKGROUND: Disease-modifying treatment (DMT) selection for people with multiple sclerosis (MS) is challenging. Neurologists and advanced practice nurses (APNs) in MS care may be facing knowledge and confidence gaps when screening patients to initiate or switch between DMTs, assessing the safety of new DMTs and monitoring for adverse events. Healthcare providers are required to demonstrate enhanced patient communication skills, to share treatment decisions and assess treatment adherence. To better inform educational interventions, there is a need to better understand these challenges and uncover their causalities. We undertook an international study across seven countries to identify challenges for neurologists and APNs that may impact DMT choices and optimum care for people with MS (pwMS). METHODS: This mixed methods study involved two concurrent data collection phases, a qualitative phase with semi-structured interviews and a quantitative phase using an online survey. Neurologists (n=333) and APNs (n=135) were recruited from Canada, France, Germany, Italy, Spain, United Kingdom and the United States. All participants had to have a minimum of two years' experience in the care of pwMS and be currently active in clinical practice. RESULTS: A triangulated analysis of qualitative and quantitative data identified multiple challenges. For APNs, these mainly related to diagnosing MS, integrating new agents in their practice, sequential DMT selection, treatment monitoring and providing personalized care. Specifically, two-thirds of APNs reported no or basic knowledge of the 2017 McDonald criteria and over half reported a knowledge gap of new DMTs available (51%) and a skill gap when integrating them into practice (58%). APNs expressed a knowledge gap of treatment sequencing (46%) and a skill gap in making decisions about sequencing (62%). Forty-four percent of APNs reported a gap in their skills of integrating patient's goals into treatment recommendations. For neurologists, the main challenges included managing side effects, aligning care to their patient's personal goals and quality of life (QoL). Specifically, over a third of neurologists reported no or basic knowledge of the characteristics of treatment failure (35%), and 32% reported no or basic skills identifying treatment failure. Skills needed to integrate patient's individual goals into treatment recommendations were reported as none or low by 39% of neurologists. In addition, there were significant differences according to years of practice in the majority (9 out of 14) of confidence items with respect to discussing specific MS-related topics with patients. Significant differences between countries were also identified. CONCLUSION: The complexity of diagnosing MS and the variety of available DMTs for pwMS lead to uncertainties, even among specialized healthcare professionals. These should be addressed through focused education and training to optimize care for pwMS.
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 | 0,089 | 0,108 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».