A Public Health Ethics Analysis of Consent as a Least Restrictive Alternative for Newborn Screening in Ontario
Bibliographic record
Abstract
Background: Public health ethics (PHE) is a theoretical lens used to analyze public health initiatives. PHE strives to find a balance between achieving population health goals and promoting individual liberty. To reach this balance PHE scholars encourage the use of least restrictive alternatives. However, least restrictive approaches are often taken for granted as presumed goods and their ability to protect individual liberty and the common good is often assumed. I examine this presumption through an examination of informed consent – implied and express – for newborn screening (NBS) in Ontario. Methods: I conducted an exploratory qualitative case study to understand how 57 individuals involved in the lobbying, development, and implementation of expanded NBS in Ontario perceive consent policy for NBS. Semi-structured interviews were audio-recorded and transcribed. Data transformation was descriptive, analytic, interpretive, and applied. Findings: Participants described their attitudes towards informed consent for NBS. PHE principles of least restrictive alternatives, effectiveness, autonomy, and social justice were key themes within implied consent data. Participants appreciated implied consent’s capacity to achieve high screening uptake, yet doubted its ability to generate informed decisions. Regarding express consent, participants introduced a host of concerns – interpreted as harm-causing – perceived to threaten NBS’s public health goals and individual autonomy. I applied the practice of consent to a PHE framework as a mechanism through which to consider consent policy for NBS in a way that moves towards achieving a balance between fulfilling public health goals and respecting individual rights and freedoms. Conclusion: What participants in advisory capacities (and those to whom they turn for input) think about consent for NBS arguably influences their recommendations to the government. Challenging informed consent as a presumed good and submitting the practice of consent to the same ethical scrutiny as the NBS program itself legitimates the concerns of those wary of consent, illuminates the perceived benefits and risks of consent, and creates an opportunity to mitigate risks before implementing or adjusting consent policy. Treating the practice of consent as an intervention in theory could work to ensure that the ideals reflected in the concept of consent are realized in NBS practice.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".