Hearing screening outcomes in Inuit children in Nunavik, Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: Hearing loss is highly prevalent among Inuit children in Canada. Hearing screening at kindergarten age has been carried out in Nunavik by trained Inuit technicians since 1986. In this study, we determined what percentage of children fail their initial hearing screening at age 5-6 years and compared this initial result with the last hearing test. We also report the type of hearing loss observed at the last test. METHODS: Results compiled in a clinical database were analysed. At age 5-6 years, 524 children (born 1990-1994, 84% coverage) were tested and 515 children were retested at a later date. Screening failure was defined as >22 dB pure tone average (.5k, 1k, 2kHz) in either ear. Observations on ear condition at the last test were used to determine type of hearing loss. RESULTS: Nineteen percent (101 children) failed the hearing screening at age 5-6 years and 12% failed on the later test. When those who failed the first test were retested, 58 had improved and 43 remained with a hearing loss. Twenty-one children who had initially passed the hearing screening were found to have a hearing loss at retest. The majority of the hearing losses were due to otitis media. CONCLUSION: Hearing screening and retesting remains necessary due to the high prevalence of hearing loss found in this population and the fluctuating nature of this problem.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".