Developing a Spoken Language Outcome Monitoring Procedure for a Canadian Early Hearing Detection and Intervention Program: Process and Recommendations
Bibliographic record
Abstract
Abstract\nPurpose: Routine spoken language outcome monitoring is one component of Early Hearing Detection and Intervention (EHDI) programs for children who are hard of hearing and learning a spoken language. However, there is no peerreviewed research that documents how spoken language outcome monitoring may be achieved, or what processes EHDI programs can use to develop these procedures. The present article describes the process used by a Canadian EHDI program and the final recommendations that were developed from this process.\nMethodology: Through consultation with the program’s stakeholders, consideration of the Joint Committee on Infant Hearing’s recommendations, and drawing on our own expertise in spoken language assessment, we developed an overall framework for monitoring spoken language. Based on the needs of the EHDI program, we conducted a scoping review and critical appraisal of norm-referenced tests to identify candidate tests to use within this framework.\nResults: We recommended a two-pronged assessment approach to measuring spoken language outcomes, including program-level assessment and individual vulnerability testing. We identified several tests that have been previously used to measure spoken language outcomes. There was little consistency in how tests were used across studies with no clear indicators as to which tests are the most appropriate to accomplish for which outcome monitoring purposes.\nConclusions: This article reports on the framework and tests used by a Canadian EHDI program to accomplish spoken language outcome monitoring. We highlight different factors that need to be considered when designing spoken language outcome monitoring procedures and the complexity in doing so. Future work evaluating the effectiveness and feasibility of our recommendations is warranted.\nKeywords: Spoken language outcome monitoring; Program Evaluation\nAcronyms: CASL = Comprehensive Assessment of Spoken Language; CDI = Child Development Inventory; CELF = Comprehensive Evaluation of Language Fundamentals; COSMIN = Consensus Based Standards for the Selection of Health Status Measurement Instruments; DEAP = Diagnostic Evaluation of Articulation and Phonology; EHDI = Early Hearing Detection and Intervention; EOWPVT = Expressive One Word Vocabulary Test; EVT = Expressive Vocabulary Test; GFTA = Goldman-Fristoe Test of Articulation; IHP = Infant Hearing Program; KLPA = Khan-Lewis Phonological Analysis; MBCDI = MacArthur Bates Communicative Development Inventories; (M)CDI = (Minnesota) Child Development Inventory; MSEL = Mullen Scales of Early Learning; PLAI = Preschool Language Assessment Inventory; PLS = Preschool Language Scale; PPVT= Peabody Picture Vocabulary Test; SLP = speech language pathologist; TACL = Test of Auditory Comprehension of Language, VABS = Vineland Adaptive Behavior Scales\nAcknowledgements: The authors have no conflicts of interest to declare. This work was funded by the Ontario Ministry of Children, Community and Social Services. The authors would like to thank the speech-language pathologists, audiologists, and program managers who contributed to the development of these procedures and recommendations. We would also like to thank Kelsi Breton for her work in evaluating articles for inclusion and exclusion.\nCorrespondence concerning this article should be addressed to: Olivia Daub, Graduate Program in Health and Rehabilitation Sciences, The University of Western Ontario, Elborn College, London, Ontario, Canada, N6G 1H1. Email: odaub@uwo.ca
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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 | 0.347 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".