Bibliographic record
Abstract
Meconium testing is an approach to detecting prenatal alcohol exposure. It has been characterized by some as a promising tool for diagnosis and inciting early interventions for people with FASD. Meconium testing inevitably reveals information about both a newborn and their gestational parent�s health. I argue that a clinical meconium testing practice risks raising several harms that are morally unjustified and would need to be addressed before the implementation of the practice. I present three ways in which meconium might risk causing or exacerbating harms to gestational parents. First, clinical meconium testing practice may risk exacerbating existing inequalities in society by placing disproportionate harm on marginalized people. This potential harm may occur due to the existence of healthcare provider biases and avoidance behavior on the part of gestational parents. Second, given the lack of accessible mental health and addictions healthcare in Canada, there is a risk gestational parents are diagnosed with an alcohol use disorder without having access to subsequent addictions treatment. Third, a routine meconium testing practice risks undermining the autonomy of gestational parents and the therapeutic relationship between them and their clinician. I offer preliminary solutions to these issues and possible directions for future multidisciplinary study.
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 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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".