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Record W4392706669 · doi:10.3390/audiolres14020026

Over-the-Counter (OTC) Hearing Aid Availability across the Spectrum of Human Skin Colors

2024· article· en· W4392706669 on OpenAlexaff
Shade Avery Kirjava, Sam Jones Faulkner

Bibliographic record

VenueAudiology Research · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsOntario College of Art and Design
FundersUniversity of California, Irvine
KeywordsOver-the-counterFood and drug administrationMedicineAudiologySkin colorHearing aidHearing lossBroad spectrumHuman skinDermatologyPharmacologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Over-the-counter (OTC) hearing aids were recently approved for sale in the United States. Research has shown that consumers prefer hearing devices that match their skin color because these devices are less noticeable. Colorism is discrimination against individuals with relatively darker skin that manifests in "skin-color" product offerings as products being offered primarily in relatively lighter colors. METHODS: This study compared images of U.S. Food and Drug Administration (FDA)-registered over-the-counter hearing aids to a range of human skin colors. RESULTS: Most over-the-counter hearing aids are only offered in relatively lighter beige colors. Few over-the-counter hearing aids are available in darker skin colors. CONCLUSIONS: These findings may represent structural bias, preventing equitable access to darker skin-color OTC hearing aids for individuals with darker skin.

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)0.004
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 score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.119
GPT teacher head0.446
Teacher spread0.327 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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