Development and evaluation of the Ghanaian digit triplet test for adult hearing screening
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".