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Record W2896342172 · doi:10.1177/1029864918806341

Neuroticism and emotion regulation through music listening: A meta-analysis

2018· article· en· W2896342172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMusicae Scientiae · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNeuroticismPsychologyMoodMeta-analysisActive listeningTraitBig Five personality traitsDevelopmental psychologyCorrelationExtant taxonSocial psychologyPersonalityCognitive psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Recent research has found that neuroticism (i.e., trait emotional instability) may dispose people to use music listening as a strategy to regulate their emotions. To estimate the magnitude of this relationship, we performed a meta-analysis (random effects model) of the extant 13 correlational studies ( k = 13) for a total of 2641 participants. Results indicated a significant small-to-medium summary effect ( r =.22, 95% CI [0.17, 0.27]) for the positive correlation between neuroticism and emotion regulation through music listening. Furthermore, there was no evidence of significant heterogeneity in effect sizes across studies. Overall, we conclude that the putative effect of neuroticism on musical emotion regulation is relatively moderate. Findings may suggest that people higher in neuroticism are more prone to use music listening as an accessible resource to regulate their negative emotions or manage whatever affects their mood in everyday life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.161
GPT teacher head0.323
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it