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Enregistrement W7019905762

Interactionality of trait-state music preference, individual variability, and music characteristics as a multi-axis paradigm for context-specific pain perception and management

2015· dissertation· en· W7019905762 sur OpenAlexaboutno aff

Notice bibliographique

RevueIowa Research Online (The University of Iowa) · 2015
Typedissertation
Langueen
DomainePsychology
ThématiqueMusic Therapy and Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMusic therapyDistractionPreferencePerceptionActive listeningTraitMusic psychologyTest (biology)Psychological interventionRhythm
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The purposes of this 3-phase study were 1) to identify salient individual variabilities and music characteristics associated with music therapy interventions for pain management, 2) to explore current pain management practices of music therapists, 3) to delineate any differences in general musical taste (trait) and context-specific music preference (state), as well as preferred music characteristics in healthy adults and cancer patients, 4) to investigate the contributions of individual variabilities, personality, behavioral coping styles, and pain levels in predicting changes from trait to state preferences and preferred music characteristics under various pain conditions, and 5) to investigate any differences in music preference patterns between healthy adults and cancer patients.\nIn Phase I, 97 music therapists completed an online questionnaire to provide quantitative and qualitative data regarding the saliency of individual variabilities and music characteristics in determining the choice of music for pain management interventions, as well as their current practices with adult populations in clinical settings. In Phase II, 50 healthy adults (33 females, 17 males) ranging in age from 40 to 70 years (M = 57.04 ± 7.99) completed a battery of tests and questionnaires, including a Participant Intake Form (demographic information, music background, listening habits), an adapted Short Test of Music Preference – Revised (STOMP-R-A), a Music Characteristics Test, the Miller Behavioral Style Scale – abbreviated (MBSS-abbreviated), and the NEO Five-Factor Inventory-3 (NEO-FFI-3). The STOMP-R-A measured the participants’ trait and state preferences for 23 music genres. The Music Characteristics Test involved a music listening portion for participants to rate their preferences for various music characteristics. The MBSS-abbreviated measured behavioral coping styles and the NEO-FFI-3 measured the five dimensions of personality. In Phase III, 35 cancer patients (24 females, 11 males) ranging in age from 42 to 70 years (M = 57.71 ± 7.07) completed the same measurement tools as the ones used in Phase II, as well as the Short-Form McGill Pain Questionnaire–2 (SF-MPQ-2), which measured ratings for chronic, acute, and neuropathic pain.\nA one-way analysis of variance was used to test for response bias amongst the music therapists in Phase I. No response bias was found. Responses were reported as sums and converted to percentages of respondents for each selected response. Qualitative responses were analyzed using open coding and thematic development techniques. An intercoder was recruited to authenticate reliability for the qualitative findings. Music therapists identified age, ethnicity, culture, and religious preferences as important individual variabilities, and tempo, rhythmic complexity, and dynamics as salient music characteristics in their ratings. The results from Phase I informed the methodology for the next two phases of this study.\nParticipants in Phases II and III were tested individually. The paired t-test was used to determine differences between trait and state music preferences across all 23 genres. The results indicated significant decreases from trait to state music preferences across music genres in both healthy adult and cancer patient groups. Calculations of the chi-square statistic and the McNemar’s test were used to detect differences between trait music preference and state music preference specific to each of the 23 genres. Multiple logistic regression analysis was used to examine the contributions of demographic factors, personality, behavioral coping style, and pain to changes from trait to state preferences and preferred music characteristics under four pain conditions. In Phase II, age, gender, and neuroticism predicted changes in trait-state preference for music genres; and gender and behavioral coping styles predicted changes in preferences for music characteristics under low-acute, high-acute, low-chronic, and high-chronic pain conditions. In Phase III, neuroticism predicted changes in trait-state preference for music genres; and age predicted changes in preferences for music characteristics under the four pain conditions.\nThe independent t-test was used to determine differences between healthy adults’ and cancer patients’ ratings of the importance of music, music background, and music listening habits. No significant differences were found between the two groups. Healthy adults and cancer patients were most familiar with country music and rated oldies and rock as their most preferred music genres. Healthy adults reported familiarity with and preferences for greater number of genres compared to cancer patients. In general, both groups indicated decreased preferences for music under pain conditions. The findings from this study emphasized the importance of considerations for the interactions of trait-state music preferences, individual variabilities, and music characteristics as a paradigm for context-specific pain management in adult clinical settings.

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)0,005
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,832
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,249
Tête enseignante GPT0,414
Écart entre enseignants0,165 · 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.

Devis d'étudeAutre devis
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

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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