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Record W4407874985 · doi:10.1080/14992027.2025.2469656

Development and evaluation of the Ghanaian digit triplet test for adult hearing screening

2025· article· en· W4407874985 on OpenAlexaff
Sesi Collins Akotey, Josée Lagacé, Christian Giguère, Katrine Sauvé-Schenk

Bibliographic record

VenueInternational Journal of Audiology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAudiologyScreening testAudiometryTest (biology)Numerical digitHearing testMedicinePsychologyHearing lossMathematicsBiologyPediatricsArithmetic

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to develop and evaluate a Ghanaian Digit Triplet Test for adult hearing screening. DESIGN: The study was conducted in two phases. Phase 1 optimised digit recognition across speech materials in a controlled environment, while Phase 2 collected normative data and assessed list equivalence using an adaptive and fixed-level method. STUDY SAMPLE: Eighty adults aged 18 to 50 years with normal hearing thresholds (≤ 15 dB HL) participated: 16 in Phase 1 and 64 in Phase 2. RESULTS: The mean SRTs were -11.3 dB for Asante-Twi and -11.4 dB for Ghanaian English, closely matching other digit triplet tests. Psychometric slopes were 17.9%/dB for Asante-Twi and 19.4%/dB for Ghanaian English. No significant differences were found in SRTs across list numbers or orders for the Asante-Twi version, indicating list equivalence and no learning effect. However, a significant learning effect in the Ghanaian English version necessitated different normative values based on participants' test exposure. CONCLUSIONS: The GDTT demonstrates consistency with other digit triplet tests while addressing specific linguistic and cultural factors. The test can improve access to hearing screening in resource-limited settings in Ghana. Further research should explore the test's applicability to a broader demographic, including children.

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 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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.364
Teacher spread0.293 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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