Functionally-based screening criteria for hearing-critical jobs based on the Hearing in Noise Test
Bibliographic record
Abstract
Effective communication is a crucial requirement in many workplaces to ensure safe and effective operations. Often, critical verbal communications are carried out in noise, which can be very challenging, particularly for individuals with hearing loss. Diagnostic measures of hearing, such as the audiogram, are not adequate to make accurate predictions of speech intelligibility in real-world environments for specific workers, and thus are not generally suitable as a basis for making employment decisions. Instead, the Hearing in Noise Test (HINT) has been identified and validated for use in predicting speech intelligibility in a wide range of communication environments. The approach to validation of the HINT takes into account the expected voice level of the talker, the communication distance between the talker and the listener, and a statistical model of speech intelligibility in real-world occupational noises. For each hearing-critical task, a HINT screening threshold score is derived upon specification of the minimum level of performance required of the workers. The HINT is available in several languages, so the tools developed are applicable in a wide range of settings, including multilingual workplaces.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".