Interventions for School-Aged Children with Auditory Processing Disorder: A Scoping Review
Bibliographic record
Abstract
(1) Background: Auditory processing (AP) disorder is associated with learning difficulties and poses challenges to school-aged children in their daily activities. This scoping review identifies interventions and provides audiologists with protocol insights and outcome measures. (2) Methods: A systematic search of both peer-reviewed and grey literature (January 2006 to August 2023) covered ten databases. Studies included had the following characteristics: (i) published in French or English; (ii) participants were school-aged, and had a normal audiogram, AP difficulties or disorder, and no cognitive, developmental, congenital or neurological disorder (with the exception of learning, attention, and language disabilities); (iii) were intervention studies or systematic reviews. (3) Results: Forty-two studies were included, and they predominantly featured auditory training (AT), addressing spatial processing, dichotic listening, temporal processing and listening to speech in noise. Some interventions included cognitive or language training, assistive devices or hearing aids. Outcome measures listed included electrophysiological, AP, cognitive and language measures and questionnaires addressed to parents, teachers or the participants. (4) Conclusions: Most interventions focused on bottom-up approaches, particularly AT. A limited number of top-down approaches were observed. The compiled tools underscore the need for research on metric responsiveness and point to the inadequate consideration given to understanding how children perceive change.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".