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Record W2093147006 · doi:10.1386/ijcm.1.3.375_1

Survey of New Horizons International Music Association musicians

2009· article· en· W2093147006 on OpenAlexaboutno aff
Don D. Coffman

Bibliographic record

VenueInternational Journal of Community Music · 2009
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationPsychologyAssociation (psychology)MusicalMusic educationBaseline (sea)Sample (material)CognitionDevelopmental psychologyPedagogyVisual artsPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

This study analysed survey responses from 1652 New Horizons International Music Association (NHIMA) musicians in the United States and Canada to better understand older adults' experiences in making music. The purpose of this study was threefold: (a) ascertain the extent of NHIMA musicians' musical backgrounds and their current involvement in music making; (b) determine perceived benefits of music making in NHIMA groups; and (c) establish a baseline for a longitudinal study that monitors NHIMA musicians' health compared with similar adults who are non-musicians to document relationships between health changes and music making. NHIMA musicians can be typified as approximately 70 years old, almost exclusively Caucasian, of average health, college educated, with above average incomes and with previous playing experience on their instruments in high school. They play their instruments on average for an hour a day. Their comments reveal that most of the respondents cite emotional well-being and benefits, followed by physical well-being, cognitive stimulation and socialization benefits. This large sample study thus corroborates the findings of previous efforts using smaller samples and provides baseline data for future research.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.130
GPT teacher head0.401
Teacher spread0.271 · 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 designObservational
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

Citations38
Published2009
Admission routes1
Has abstractyes

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