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Enregistrement W2788874449

Depressive symptomatology and lifelong music experience: a cross-sectional study

2017· dissertation· en· W2788874449 sur OpenAlexaboutno aff
Jennifer Asselstine

Notice bibliographique

RevueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Langueen
DomainePsychology
ThématiqueMusic Therapy and Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCross-sectional studyPsychologyLifelong learningClinical psychologyDevelopmental psychologyMedicinePedagogy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Depression is a serious mental health disorder, with enormous costs to individuals and society. According to the 2012 Canadian Community Health Survey, approximately 1.9 million Canadians have reported a major depressive episode within the last twelve months.1 While alarming, this figure does not capture the level of subclinical depression (or depressive symptomatology) in the Canadian population. Some estimates have suggested that the prevalence of subclinical depression is much higher, at 22%. 2 Hence, factors that mediate or prevent the development of subclinical depression are of interest. Music therapy has been shown to reduce feelings of depression in elderly patients, but it is unclear what the effect of playing music as a hobby may have on the development of depression. 3-11
\nSome research has suggested that musicians may suffer from a disproportionate amount of mental health disorders when compared to their non-musical counterparts. 12-14 Music is frequently used as a way to reduce stress, and it is possible that individuals with serious mental health disorders (MHD) turn to music as a means of escape. 15-17 While several studies have suggested that professional musicians have higher levels of depression than their non-musical counterparts, this research has been conducted in musical professionals alone. 12-14 No study has evaluated the potential relationship between playing music as a hobby and the development of subclinical depression in university students. Based on studies regarding music therapy, it is highly plausible that music may offer a significant means by which to reduce the presence of depressive symptomatology. Further study on the impact of musicianship on the development of mood disorders is warranted.
\nA cross-sectional survey was used to pose questions to a group of university students regarding their experience playing a musical instrument over their lifetime and depressive symptomatology. Within the musician cohort, a further investigation was conducted into the relationship between improvisational ability and presence of mental health disorders. Additional information regarding potential confounding variables was also captured in the survey. Music has been shown to possess therapeutic qualities in certain demographics, but its association with depressive symptoms in average university students is unknown. 3-11, 18-21 Exploring this association will help to direct larger studies, and will allow us to hypothesize potential roles of music in relation to subclinical depression.
\nShort-term mood-boosting effects of music have been widely reported in the literature; however, these findings are often studied in a therapeutic context, and not treated as a lifelong exposure.3-11, 18-21 This study is the first of its kind to employ a cross-sectional survey to ascertain different levels of musical exposure, as well as the musician?s improvisatory capabilities. This study attempts to take a sample of average Canadian students and explore the potential relationship between lifelong music exposure and depressive symptomology.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,286
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,064
Tête enseignante GPT0,372
Écart entre enseignants0,309 · 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'étudeObservationnel
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
Publié2017
Routes d'admission1
Résumé présentoui

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