OPIOID-DEPENDENT MOTHERS IN MEDICAL DECISION MAKING ABOUT THEIR INFANTS’ TREATMENT: WHO IS VULNERABLE AND WHY?
Bibliographic record
Abstract
Infants born to opioid-dependent women are typically admitted to neonatal intensive-care units for management of neonatal abstinence syndrome (NAS), and their treatment requires medical decision making. It is not only the infants’ vulnerability, in terms of their incompetence and medical condition, that is present in those circumstances, but also the mothers’ situational vulnerability, which arises with the possibility of their engagement in medical decision making regarding their infants. Vulnerability is a concept that has often, if not always, been traced back to individuals. In this paper, we suggest that in some cases evaluations and attributions of vulnerability to either individuals or populations fall short of capturing all aspects of vulnerability. We ask whether this individual-based evaluation is sufficient for identifying all the vulnerabilities arising in the situation. Moreover, we suggest that the “unit” of vulnerability attribution, typically a person who is a likely target of harm and/or moral violations, should not simply be reduced to the individual. Rather, the unit should in some cases be seen as constituted by an entity that is interpersonal in nature. The kind of real vulnerability that we identify in this paper is inherently embedded in a dyadic relationship, and notions of vulnerability that decompose social relations into individuals run the risk of missing the vulnerability in question. We elaborate this kind vulnerability by discussing of role of opioid-dependent mothers in decision making about their infants’ treatment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".