Phoneme Training for Adult Cochlear Implant Users: A Review of the Literature and Study Protocol
Bibliographic record
Abstract
PURPOSE: This study describes a protocol for a novel individualized phoneme training program for adult cochlear implant (CI) users, based on individual phoneme confusion errors. The protocol is underpinned by a literature review on phoneme training and a focus group with adult CI users. METHOD: = 7) was then conducted to gain insights into their experiences of auditory training post-implantation and recommendations for future training programs. The knowledge gained from the literature review and focus group was used as the foundation for a novel, individualized phoneme training program for adult CI users, for which the protocol is described in this study. RESULTS: A review of the literature shows that phoneme training in adult CI users has variable outcomes for on-task and off-task measures. Overall, the concept of individualized training relates to adaptive difficulty within training tasks and not to tailoring training content to participants' individual needs, as indicated by clinical outcomes. The focus group revealed that participants want to be able to track their training progress, have training content tailored to their individual needs, and expressed a preference for shorter training sessions. CONCLUSIONS: Using learnings from a literature review and focus group, this study describes a protocol for a novel, individualized phoneme training program for adult CI users. Study findings from this phoneme training program will be disseminated when available. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24392863.
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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 | 0.049 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".