Economic Rationality in Decision-Making Regarding Newborn Screening: A Case Study in Quebec
Bibliographic record
Abstract
Health systems in high-resource countries recognize the importance of making decisions about the services offered to the population based on scientific evidence. Producing this evidence is especially challenging in areas such as newborn care where the frequency of conditions is rare. However, methodological advances in the field of economic evaluation could change how this evidence is used in decision-making. This study aimed to investigate how decision-makers in the Canadian province of Quebec perceive the value of recent advances in economic evaluations for perinatal studies and how these advances might affect the offer of neonatal interventions in the public health care system. A qualitative study was conducted. A total of 10 policymakers were interviewed. A neo-institutional conceptual framework highlighting three dimensions, structure, power, and interpretive schemes, was used for data collection and analyses. Structural factors, interpretative schemes, and power management between the groups concerned concur to ensure that providing services to newborns is not hindered by the difficulty of producing evidence. They also ensure that the decisions regarding which disease to screen for take into consideration the specificity of neonatology, in particular, the social value given to children not captured by available evidence.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".