Bibliographic record
Abstract
Audiologists are primary providers of hearing healthcare. As part of their scope of practice, they perform detailed evaluations of the auditory function, prescribe hearing aids and other assistive listening devices, offer counseling and aural rehabilitation services, and participate in hearing loss prevention activities. In Canada, Audiology is taught at the Master’s Degree level, typically in an intensive 2-year program. Applicants are from a wide variety of disciplines (e.g. psychology, linguistics, education, health sciences) where acoustic measurements and use of electroacoustic instruments (e.g. audiometers, hearing aid analyzers) are not part of the curriculum. From a pedagogical perspective, acoustical standards provide an important vehicle for introducing proper terminology and keys concepts in acoustics and instrumentation. Through laboratory assignments and other learning activities, hands-on experience can be gained on the operation, calibration and tolerance limits of clinical instruments. From a professional perspective, self-regulated health colleges such as the College of Audiologists and Speech-Language Pathologists of Ontario issue Practice Standards and Guidelines (PSGs) and/or position statements on equipment use and servicing requirements for their Members that refer to specific acoustical standards, especially from the ANSI S3 series on Bioacoustics. This paper will focus on the use of audiometry standards (e.g. ANSI S3.1, ANSI S3.6) and hearing aid characteristics (e.g. ANSI S3.22) in the Audiology classroom as well as other CSA and ISO standards relevant to the profession.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".