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Record W4385332373 · doi:10.47513/mmd.v15i3.916

Connecting through music: A systematic review of the use of music to reduce loneliness during the COVID-19 pandemic

2023· review· en· W4385332373 on OpenAlex
Rowena Cai, Gohar Zakaryan, Kevin Zhang, Rachael Finnerty

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

VenueMusic and Medicine · 2023
Typereview
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLonelinessSocial isolationFeelingPandemicCoronavirus disease 2019 (COVID-19)Isolation (microbiology)Active listeningPsychologyMusicalClinical psychologySocial psychologyMedicineArtPsychotherapistVisual arts

Abstract

fetched live from OpenAlex

Social interactions were limited due to COVID-19 restrictions resulting in a high prevalence of loneliness and social isolation. The purpose of this systematic review is to investigate the impact of engaging in music on the experience of loneliness during the COVID-19 pandemic. We included nine articles with a total of 16,176 participants, all of which reported upon the impact of musical engagement in the form of music listening or music-related activities on loneliness. The average age of participants was 43 ± 15 years, and 37% were male. Eight studies (88.9%) reported that music engagement reduced loneliness. This systematic review demonstrates that music may have had a beneficial impact on loneliness during the COVID-19 pandemic. Our findings suggest that the use of music is an accessible method to cope with feelings of loneliness and improve overall wellbeing during times of social isolation.

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)0.003
metaresearch head score (Gemma)0.003
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.523
GPT teacher head0.491
Teacher spread0.032 · 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