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Record W7028501373

Evaluating Spoken Language Outcome Monitoring Data Collection in Ontario’s Infant Hearing Program

2024· article· en· W7028501373 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsSpoken languageData collectionIntervention (counseling)Hearing lossOutcome (game theory)Language developmentVariation (astronomy)
DOInot available

Abstract

fetched live from OpenAlex

International recommendations suggest that all children with permanent hearing loss enrolled in Early Hearing Detection and Intervention programs should have their language development routinely monitored. This study was a retrospective database review designed to understand the reasons behind a significant absence of spoken language outcome monitoring data in the Ontario Infant Hearing Program’s database. The study aims included evaluating variations in language outcome monitoring implementation, exploring regional differences in procedure adoption, and identifying the reasons why speech-language pathologists were unable to assess eligible children (and any regional variation in these reasons). Results suggested that implementation improved significantly from one year to the next and that there were important regional differences in procedure adoption. Additionally, SLPs primarily cited child factors as the main reason for not assessing certain children. There were no notable regional variations in the reasons reported by SLPs for not completing the procedures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.082
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.358
GPT teacher head0.415
Teacher spread0.057 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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