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Record W2103001378 · doi:10.4103/1463-1741.112361

Noise and health in vulnerable groups: A review

2013· review· en· W2103001378 on OpenAlexaff
Irene van Kamp, Hugh Davies

Bibliographic record

VenueNoise and Health · 2013
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVulnerability (computing)Learned helplessnessNoise (video)PsychologyNoise exposurePublic healthMedicinePsychiatryClinical psychologyDevelopmental psychologyHearing lossAudiologyComputer securityNursingComputer science

Abstract

fetched live from OpenAlex

Vulnerable or susceptible groups are mentioned in most reviews and documents regarding noise and health. But only a few studies address this issue in a concrete and focused way. Groups at risk most often mentioned in the literature are children, the elderly, the chronically ill and people with a hearing impairment. The other categories encountered are those of sensitive persons, shiftworkers, people with mental illness (e.g., schizophrenia or autism), people suffering from tinnitus, and fetuses and neonates. The mechanism for this vulnerability has not been clearly described and relevant research has seldom focused on the health effects of noise in these groups in an integrated manner. This paper summarizes the outcomes and major conclusions of a systematic, qualitative review of studies over the past 5 years. This review was prepared for the 10 th Conference on Noise as a Public Health Problem (ICBEN, 2011). Evidence is reviewed describing effects, groups assumed to be at risk, and mechanisms pertaining to noise sensitivity and learned helplessness.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.220
GPT teacher head0.525
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations199
Published2013
Admission routes1
Has abstractyes

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