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Record W2101719261 · doi:10.1093/jmt/43.4.295

An Experimental Investigation of the Effects of Preferred and Relaxing Music Listening on Pain Perception

2006· article· en· W2101719261 on OpenAlexaboutno aff
Lex A. Mitchell, Raymond MacDonald

Bibliographic record

VenueJournal of Music Therapy · 2006
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningPsychologyAudiologyPerceptionRelaxation (psychology)Cold pressor testPain perceptionFeelingVisual analogue scaleStimulus (psychology)Pain tolerancePhysical therapyThreshold of painAnesthesiaSocial psychologyCognitive psychologyMedicinePsychotherapistInternal medicine

Abstract

fetched live from OpenAlex

This study investigates the effects of music listening on perception and tolerance of experimentally induced cold pressor pain. Fifty-four participants (34 females, 20 males) each underwent 3 cold pressor trials while listening to (a) white noise, (b) specially designed relaxation music, and (c) their own chosen music. Tolerance time, pain intensity on visual analog scale, and the pain rating index of the McGill Pain Questionnaire and perceived control over the pain were measured in each condition. While listening to their own preferred music, male and female participants tolerated the painful stimulus significantly longer than during both the relaxation music and control conditions. However, only female participants rated the intensity of the pain as significantly lower in the preferred music condition. Both male and female participants reported feeling significantly more control when listening to their preferred music. It is suggested that personal preference is an influential factor when considering the efficacy of music listening for pain relief.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.312
Teacher spread0.276 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations209
Published2006
Admission routes1
Has abstractyes

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