Meaningful Music in Dementia Care: Anchoring Within Transient Situations
Notice bibliographique
Résumé
It is currently estimated that there are 54 million people living with dementia in the world, and that there will be 130 million people living with the condition by 2050 (Alzheimer’s Society, 2020). In the United Kingdom alone there are around 850,000 people living with a formal diagnosis of dementia, and it is estimated that around a quarter of patients in UK hospitals have some form of dementia. (Alzheimer’s Society, 2009). There is a clear need to support the emotional care of patients living with dementia alongside their medical care in order to reduce the negative impact that hospital ward environments can have on their wellbeing. The use of meaningful music in dementia care is one such way of supporting socio-emotional needs alongside medical care. This thesis demonstrates how. The primary research question was to understand how patients in hospital who are also living with dementia engage with music and with what consequences, with regards to how music can support the relational aspects of care. Adopting elements of ethnography and grounded theory methodologies, the research focused specifically on the ways that live music was seen to support the wellbeing of patients living with dementia, through focusing on two specific objectives: 1. To examine how the environment and particularly the soundscape of a hospital ward affects the ward culture and hospital experience for patients living with dementia, and for care staff. 2. To explore how patients living with dementia respond to live music performances, and the ‘spin off’ effects of this for care staff. The research was conducted at one major acute-care hospital in England within two Elderly Care Units over a period of four months from November 2016 to March 2017. The data was formed from 104 hours of observations over 37 days, of 201 people, consisting of patients (with and without dementia), staff and visitors, including 54 music sessions performed by six musicians. In addition to this, one focus group was conducted with five members of staff, as well as one-off interviews with four musicians. The findings of the thesis reveal that familiar music can create positive associations for individuals living with dementia and their surrounding environment, creating increased feelings of emotional wellbeing. Additionally, shared experiences through music and its associations with reminiscence, were found to support social connections between the individual living with dementia and their care practitioners, which resulted in enhanced mutual understanding, that can be seen to continue after the music has ended. Through these shared experiences and increased connections, music is therefore able to create an environment which is more conducive to positive emotion. The thesis concludes that familiar, meaningful music delivered by musicians who can adapt their performance and connect with their ‘audience’ on an emotional level can support the wellbeing of people living with dementia within hospital wards, and possibly support staff to provide person-centred and relationship-centred care. The study presents a wide lens picture of music’s role within two dementia friendly hospital wards. I consider this to be ‘wide lens’ in that it looks beyond just the musical performance itself, to demonstrate music’s role in creating positive environments conducive to wellbeing, to create and sustain social connections, and to reduce feelings of frustration, isolation and agitation; outcomes that last long after the music has ended. The results are useful in the fields of music sociology, medical sociology, music therapy, community music, as well as practical applications in nursing and healthcare. Whilst the research was conducted in acute healthcare it is also anticipated that it presents wider implications for residential and domiciliary care of people living with dementia.
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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,020 |
| Communication savante | 0,013 | 0,010 |
| Science ouverte | 0,002 | 0,017 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».