Usability and Feasibility of a Spoken Language Outcome Monitoring Procedure in a Canadian Early Hearing Detection & Intervention Program: Results of a 1-Year Pilot
Bibliographic record
Abstract
Purpose: Best practice recommendations for Early Hearing Detection and Intervention (EHDI) programs include routine spoken language outcome monitoring. The present article reports on pilot data that evaluated the usability and feasibility of a spoken language outcome monitoring procedure developed for Ontario’s Infant Hearing Program (IHP). This procedure included both Program-level monitoring using omnibus language tests from birth to 6 years of age and individual vulnerability monitoring of key domains of spoken language known to be at risk in children who are deaf or hard of hearing.\nMethodology: Speech-language pathologists (SLPs) in the IHP piloted the new procedures for one year and provided feedback on the procedure through surveys at the end of the pilot.\nResults: Data was suggestive that the Program-level procedure might be sensitive to change over time and known predictors of spoken language outcomes. Some, but not all, Program-level test scores were predicted by the presence of additional developmental factors. None of the test scores were significantly predicted by severity of hearing loss. Depending on the tests and scores used, some aspects of the Program-level procedure were sensitive to change over time. There was insufficient evidence to support individual vulnerability monitoring. SLPs reported significant concerns about the time involved in implementing both procedures.\nConclusions: This article describes preliminary evidence suggesting that the Program-level procedure might be feasible to implement and useful for evaluating EHDI programs. Future evaluations are needed to determine whether the procedure can be accurately implemented to scale in the IHP, and whether the data that results from the procedure can meaningfully inform stakeholders’ decision-making.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".