Getting on with Your life with MS: Development, refinement and preliminary testing of a self-management workbook for people living with multiple sclerosis
Notice bibliographique
Résumé
Multiple sclerosis (MS), an auto-immune disease of the central nervous system, is the leading cause of neurological disability among young adults in Canada. The effect of MS is profound and is similar for people across nations, socioeconomic gradients, levels of education, and occupations. Its presentation varies widely from person to person, with symptoms ranging from mild sensory alterations to severe disability limiting activity and restricting participation in life's roles. These manifestations of MS have a direct impact on quality of life (QOL). The main priorities of people with MS are to remain independent and empowered to participate in their communities and their care. In order to maintain their QOL as high as possible, people with MS recognize the importance of maintaining a healthy life style, yet research shows that few are sufficiently physically active to maintain health in the face of a progressive disease. This indicates a gap between information (knowledge) and action, a gap that needs to be filled. To bridge that gap and address the wide range of topics important to people with MS, a knowledge tool based on principles of self-management was developed. Self-management is a lifetime task where patients are coached to maintain wellness in their foreground perspective, rather than illness, through development of five core skills: problem solving, decision making, resource utilization, forming patient/health care provider partnership, taking action. To put those skills to use, a person needs to gain the ability to self-assess, identify and implement strategies to improve, and monitor progress. The first aim of this thesis was to develop a workbook for people living with MS entitled Getting On With Your Life With MS: A Guide To Taking Charge Of Your Health (GETONMS©) following the principles and skills of self-management and optimize its functionality. The development and optimization of GETONMS© is presented in Manuscript 1 along with a 10-stage process that would allow others to use this methodology to develop and refine self-management educational material for other patient populations. GETONMS©, presented in Chapter 5, is a workbook that covers 43 topics identified as important for the quality of life of people with MS. Clinicians and researchers who evaluated the workbook have approved of the content and format and showed interest in offering the final product to their patients. Preliminary evaluation showed that the content and format fit the needs of the users and little or no support is needed to use the workbook.The next objective of this thesis was to gain insight into the processes through which engagement with the workbook produces change in QOL. Manuscript 2 explored how people with MS valued GETONMS© as well as the potential of GETONMS© to produce a response shift in QOL over a 2 month period. The information gathered from the analysis of values and changes in QOL in the form of response shift helped design a feasibility study to be conducted on the internet in order to make the workbook accessible to as many as possible.Manuscript 3 presents the protocol for the randomized feasibility study as well as data from the first 23 subjects recruited to identify strengths and limitations of using social media to enroll people with MS into an internet-based study of self-management. This interim analysis was conducted to allow for modifications to the protocol to be made if needed. It showed that social media recruitment strategy reached a patient population similar to the traditional in-person recruitment sample but also recruited participants into the study over twice as fast. The three manuscripts and the GETONMS© self-management workbook presented in this thesis provide evidence towards global self-management research, social media recruitment, and will allow for the design of a main trial evaluating GETONMS©. It also produced a workbook that will be made available to people with MS soon.
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 | 0,011 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».