Towards a roadmap for implementing a self-management approach for people with multiple Sclerosis in Saudi Arabia
Notice bibliographique
Résumé
Multiple sclerosis (MS) is one of the most disabling neurological conditions affecting young adults. MS is becoming more common in Saudi Arabia where specialized health services are still being developed. Many people with MS in Saudi Arabia look for help where they can and risk getting information from unreliable sources. In such a context, self-management would be crucial for reducing the impact of MS. Success at self-management requires acknowledging the specific problems, recognizing that solutions are possible, and contextualizing self-management for the individuals within their culture.This PhD thesis aims at setting a roadmap to aid in the translation of knowledge about self-management and MS in Saudi Arabia into an actionable implementation plan for a self-management intervention culturally relevant for people with MS. The overall objective is to contribute evidence towards the optimal structure, process, and outcomes for a self-management intervention for people with MS in Saudi Arabia.This PhD work comprises five studies. The first study aimed at identifying challenges related to current self-management interventions through a systematic literature and meta-analysis on how appealing are these programs to patients, and what contributes to participation in them. The second step in translating this knowledge was to explore the MS impact and challenges in self-management for women with MS in Saudi Arabia through a qualitative cross-sectional study. Women identified a range of symptoms similar to those reported worldwide, but the emotional burden predominated. Gaps in the healthcare system were identified and many had sought help elsewhere using internet sources. Stories narrated during discussions were found to be an effective source of revealing how values, goals, and expectations guided choices for personal strategies for self-management. Therefore, the third study used narrative analysis of stories and metaphors to illuminate women’s self-management journeys. Fatigue is one of the most distressing symptoms reported by people living with MS around the world and its impact was confirmed for people in Saudi Arabia. Therefore, identifying the best ways of measuring this complex construct was a prerequisite for developing effective interventions. Thus, the aim of the fourth study was to identify the best method of capturing information about fatigue in MS using items from patient-reported outcome measures. Taking advantage of existing data from a MS study conducted in Montreal on 189 people with MS, analyses identified two separate fatigue constructs, perception of physical fatigue and perception of mental fatigue, and items that reflected these distinct constructs. These four studies provided foundational information that shaped the final study, which was also centered on the importance of eliciting knowledge that is directly associated with real-world needs. Therefore, by following a participatory research approach, the fifth study aimed at identifying key pieces of information that need to be gathered from the MS population in Saudi Arabia to inform the structure, process and outcome of a self-management program. The partnership resulted in survey items that were pilot tested on 101 individuals with MS recruited in two ways: using social media and MS clinics in Saudi Arabia. The pilot survey provided preliminary estimates of prevalence and also information on clarity of items. Cognitive interviewing was conducted with a sub-sample of 13 individuals completing the survey to remedy unclear items. This thesis contributed evidence towards identifying care gaps that could be filled through a self-management approach and ascertained the extent to which existing MS self-management interventions could be adapted to cover this content in a manner that is culturally suitable to MS Saudi Arabian population.
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 | 0,029 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
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 ».