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Assessment in the Creative Arts Therapies: Designing and Adapting Assessment Tools for Adults with Developmental Disabilities

2010· article· en· W296060169 sur OpenAlexaboutno aff
Liz Moffitt

Notice bibliographique

RevueCanadian journal of music therapy · 2010
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueArt Therapy and Mental Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésThe artsPsychologyPopulationMedical educationMedicineVisual artsArt
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Assessment in the creative arts therapies: Designing and adapting assessment tools for adults with developmental disabilities Stephen Snow and Miranda D'Amico (Editors) Charles C. Thomas (2009) ISBN 978-0-398-07887-4This book is a unique contribution the challenging area of creating assessment tools that are arts-centered using various creative arts therapies. It is a timely response the ever-growing demand in the clinical arenas where funding is more and more restricted, for demonstrated effectiveness of our work, i.e., evidence-based practice using controlled, replicable studies. The authors acknowledge the difficulty this presents for many arts therapists, who see themselves first as artists, who are neither interested in nor trained as researchers, who see research as limited in its ability represent the whole person, and can therefore at best be limiting and at worst can be extremely misleading. I feel that the authors have done an excellent job of tangling with these issues as they share their processes, working in real-life clinical situations, and have come up with some excellent solutions that are both replicable and address the whole person.The book is composed of seven chapters, five of which address assessment using five different arts therapies, with the same population of adults with intellectual and developmental disabilities who were all participants in a 3-year program at the Centre for the Arts in Human Development at Concordia University in Montreal, Quebec. After a 4-year pilot project exploring assessment in the arts therapies, the Centre was given a threeyear Social Science and Humanities Research Council (SSHRC) grant formalize their findings and this book is the result of their 7 years of work!The first chapter by the editors addresses the challenges of assessment, particularly using the Creative Arts Therapies, and it introduces us the Centre for the Arts at Concordia University. In the second chapter Lister and Rosales describe how they adapted the Kinetic-House-Tree-Person drawing for use with the Centre's population. Chapter 3, written by music therapist Shelley Snow, describes the process of developing an assessment tool for their clients. I will return this chapter in more detail. In Chapter 4, Stephen Snow, Maeng-Cleveland, and Steinfort describe the journey towards adapting the Diagnostic Role-Playing Test and they include two case vignettes that exemplify the results of this process. Chapter 5 focuses on the analysis of movement, written by dance therapists Sack and Bolster. Art therapist and sandtray specialist Tanguay describes how the sand tray can be used and adapted for assessment with the Centre's population. Again, case vignettes are presented along with an analysis of the results. And finally, in Chapter 7, D'Amico, Miodrag, and Dinolfo summarize the various results of these arts therapies and correlate them with Quality of Life measures that they used over the same three-year period. Their results strongly reinforced the value of combining the use of the various creative arts therapies assess, support and develop each client's personal growth.Since we are music therapists, I will describe in more detail Shelley Snow's chapter on developing a music therapy assessment tool. Snow begins with a brief discussion about the issues of evidence-based research and the criticisms of qualitative researchers for failing to encompass the richness of the Music Therapy process in all its dimensions. Snow fully agrees with their views, but feels that evidence-based research can describe and measure some things, and for that reason, it can be valuable, while acknowledging that it cannot describe and measure all things about a person. …

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,202
Score d'incertitude au seuil0,810

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,083
Tête enseignante GPT0,292
Écart entre enseignants0,209 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2010
Routes d'admission1
Résumé présentoui

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