How the World’s Children Hear: A Narrative Review of School Hearing Screening Programs Globally
Bibliographic record
Abstract
OBJECTIVE: School hearing screening may mitigate the effects of childhood hearing loss through early identification and intervention. This study provides an overview of existing school hearing screening programs around the world, identifies gaps in the literature, and develops priorities for future research. DATA SOURCES: A structured search of the PubMed, Embase, and Cochrane Library databases. REVIEW METHODS: A total of 65 articles were included according to predefined inclusion criteria. Parameters of interest included age groups screened, audiometric protocols, referral criteria, use of adjunct screening tests, rescreening procedures, hearing loss prevalence, screening test sensitivity and specificity, and loss to follow-up. CONCLUSIONS: School hearing screening is mandated in few regions worldwide, and there is little accountability regarding whether testing is performed. Screening protocols differ in terms of screening tests included and thresholds used. The most common protocols included a mix of pure tone screening (0.5, 1, 2, and 4 kHz), otoscopy, and tympanometry. Estimates of region-specific disease prevalence were methodologically inaccurate, and rescreening was poorly addressed. Loss to follow-up was also a ubiquitous concern. IMPLICATIONS FOR PRACTICE: There is an urgent need for standardized school hearing screening protocol guidelines globally, which will facilitate more accurate studies of hearing loss prevalence and determination of screening test sensitivity and specificity. In turn, these steps will increase the robustness with which we can study the effects of screening and treatment interventions, and they will support the development of guidelines on the screening, diagnostic, and rehabilitation services needed to reduce the impact of childhood hearing loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".