Newborn Hearing Screening in Québec, Canada
Bibliographic record
Abstract
PURPOSE: This study discusses the history and current state of the newborn hearing screening program in Québec and aims to assess general challenges associated with establishing universal newborn hearing screening (UNHS) programs. METHOD: We reviewed the statistics of the occurrence and long-term effects of congenital hearing loss and the immediate and long-term benefits of UNHS and its limitations. The resources for this study included financial reports related to establishing UNHS in different health care systems; Canadian provincial, territorial, and federal regulations and publications; local and nationwide media; and interviews health care staff and program managers. RESULTS: Because of its benefits and its cost-effectiveness, UNHS programs have been implemented in many health care systems around the world. Despite Canada's success in offering a wide array of health care services to its citizens, certain provinces trail behind others in developing UNHS programs. Although there have been recent improvements in the screening rate of the province of Québec, nearly half of all Québec newborns continue to not be screened for hearing loss. The reasons for the current low screening rate include delays in implementation, information-technology complications, operating costs, and lack of public awareness. CONCLUSIONS: For UNHS to be implemented in a timely fashion, those involved in the process should first understand what challenges may arise. Québec's experience with this process may provide useful lessons for other health care systems.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".