Facilitating Emergent Literacy Skills in Children with Hearing Loss
Bibliographic record
Abstract
PurposeTo (a) familiarize readers with the components of emergent literacy and the impact hearing loss may have on the development of these skills; (b) demonstrate the importance of parent–professional collaboration and show how specific literacy-based activities can be integrated into existing daily routines and intervention programming; and (c) discuss how literacy-based activities can be modified to simultaneously target auditory and listening development.MethodThe paper begins with a narrative review of the current literature on the components of emergent literacy and development of this skill in children with hearing loss. Within this review, auditory-based strategies that may be used to facilitate emergent literacy in children with hearing loss who use spoken communication are described. Readers are directed to contact the authors for more detailed examples of activities and strategies.ConclusionsBuilding emergent literacy skills in children with hearing loss is contingent upon parents and professionals collaborating to develop specific literacy-based activities that can be incorporated into children's existing speech and language programmes and daily routines. These activities can be tailored to meet the individual needs of children with hearing loss and simultaneously address clinically relevant goals that maximize the development of auditory and listening skills.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".