Listening in noise training in children with auditory processing disorder: exploring group and individual data
Bibliographic record
Abstract
Purpose: The purpose of this study was to examine the impact of auditory training in noise on auditory behaviors and life habits in children with auditory processing disorder.Methods: Ten children with auditory processing disorder underwent an auditory training program in noise and six children with auditory processing disorder comprised a control group. Before and after training, participants were tested on sentence identification in noise and auditory evoked late latency responses. Participants teachers completed two questionnaires on children’s auditory behaviors and life habits.Results: Participants were more tolerant to noise as the training sessions progressed. Significant between-group differences were found in P1 and N2 latency measures, independent of measurement time. The observed data trends suggest that some participants improved their performance on the sentence identification task in noise as well as on some electrophysiological parameters. No significant differences in questionnaire scores were found between groups or measurement times. However, one questionnaire showed significant between-group differences for certain questions.Conclusions: Listening in noise can improve with training for children with auditory processing disorder. However, this training program might be beneficial for some, but not all, children with auditory processing disorder. More data are needed to verify individual data trends.Implication for rehabilitationA structured program was developed to improve the ability of children with auditory processing disorder to listen in noise.Intervention can be beneficial for improving auditory behaviors in some children with auditory processing disorder.A limited number of questions on children’s auditory behaviors asked to teachers appears to be more sensitive to intervention-related improvement compared to questions on life habits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".