Developing a Spoken Language Outcome Monitoring Procedure for Early Hearing Detection and Intervention Programs
Bibliographic record
Abstract
Early Hearing Detection and Intervention programs are associated with improved spoken language outcomes for children who are deaf/hard-of-hearing. Best practice recommendations call for regular spoken language outcome monitoring to support decision making for all stakeholders (families, audiologists, speech-language pathologists, and program managers).\nDespite the clear calls for spoken language outcome monitoring, there is no peer-reviewed guidance as to how Early Hearing Detection and Intervention programs can best accomplish this monitoring. This dissertation evaluates the assumptions underlying spoken language outcome monitoring and contributes a new procedure developed for a Canadian Early Hearing Detection and Intervention program: the Ontario Infant Hearing Program.\nWhether decisions can be validly made using assessment data underpins the tenability of spoken language outcome monitoring. Chapter 2 considers test misuse across the profession of speech-language pathology from test design to clinical practice. I argue that a conceptual validity framework is one potential solution. This framework is applied throughout the dissertation.\nChapter 3 aims to develop a spoken language outcome monitoring procedure to support the Ontario Infant Hearing Program. This chapter describes the process I engaged in, including a scoping review and critical appraisal of norm-referenced spoken language tests, to develop an outcome monitoring procedure for the Infant Hearing Program.\nPrior to implementing the recommended procedures province-wide, the Infant Hearing Program needed evidence as to whether the recommendations (a) meaningfully inform stakeholder decisions and (b) are feasible to implement. Chapter 4 reports on a pilot implementation of the recommended procedures and speech-language pathologists’ perceptions of it.\nDuring development of the procedure outlined in Chapter 3, one of the key vulnerabilities I recommended to monitor was early vocal development in children who are younger than 2 years. Chapter 5 is a survey study capturing the clinical questions speech-language pathologists’ have about early vocal development of children who are deaf/hard-of-hearing to inform future projects to assess the validity of candidate vocal development assessments.\nOverall, this dissertation contributes a spoken language outcome monitoring procedure for Early Hearing Detection and Intervention programs and highlights the tension between decisions, psychometrics, and implementation, in accomplishing spoken language outcome monitoring to inform best practice recommendations.
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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 | 0.284 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".