Reading achievement and deaf students with cochlear implants
Bibliographic record
Abstract
Objectives The purpose of this study was to investigate the reading outcomes of a Canadian cohort of school-aged deaf learners with cochlear implants (CIs). The goal was to investigate whether achievement approached that of hearing age peers and identify demographic factors influencing performance.Methods Participants represent a subset of 13 students with CIs from a larger sample of 70 deaf students in grades four through 12 educated in inclusive settings within a large school board in central Canada. Data sources included demographic information, teachers’ ratings on the Categories of Auditory Performance (CAP), and scores from the Woodcock-Johnson III Diagnostic Reading Battery [WJ III-DRB].Results/Discussion: Participants performed within the low average range in all areas except for Phonological Awareness, which was in the low range; however, there was wide variability in scores across participants. None of the demographic variables (e.g. home language, additional disabilities) had a statistically significant association with performance, although older students had higher mean scores on the Phonological Awareness cluster.Conclusion These findings add to the body of research on literacy achievement and cochlear implantation, providing evidence that this technology has a significant positive effect on outcomes for a population that has heretofore underperformed in this area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".