Using a Telehealth Medium for Objective Hearing Testing: Implications for Supporting Rural Universal Newborn Hearing Screening Programs
Bibliographic record
Abstract
Telepractice is commonly used in other health professions to dispense a large number of services. These services include diagnostics, rehabilitation, and counseling. Telepractice is increasingly available to practitioners serving rural areas and can be modified for many health applications. Yet telepractice is not widely used by audiologists. This is somewhat surprising because audiology seems poised to offer telepractice using both synchronous and asynchronous technology. Furthermore, telepractice may be particularly effective for enhancing a universal newborn hearing screening (UNHS) program in rural areas. Specifically, rural communities often lack the proper personnel and program continuity to serve newborns with hearing loss effectively. Telepractice may be a method that can ameliorate these problems. Presently, Utah State University is evaluating the value of telepractice for UNHS services. Initial data obtained from this project are promising and support the validity of telepractice with infant hearing services. However, research with a much larger group of subjects will be required before telepractice can be used with confidence in a UNHS program. KEYWORDS Universal newborn hearing screening - telepractice - telehealth - telemedicine - hearing loss
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".