Toward Gender-inclusive, Nonjudgmental Alcohol Interventions for Pregnant People: Challenging Assumptions in Research and Treatment
Bibliographic record
Abstract
: Epidemiological and clinical evidence clearly indicates that binge and/or heavy alcohol use while pregnant can be dangerous for the fetus. As such, there is a large body of research evaluating interventions to address harms associated with alcohol use during pregnancy. Unfortunately, based on our assessment of the scientific literature in this area, including a reading of three high-impact systematic reviews, there are several key areas where the language being used is hindering efforts to address alcohol harms during pregnancy in nonjudgmental and gender-inclusive ways. In this commentary, we describe four areas where intervention research in this area can benefit from a thoughtful refinement of the use of gender-inclusive and nonjudgmental language. We also describe how, in failing to do so, interventions to address alcohol use during pregnancy will continue to be evaluated and designed without a sufficient understanding of how gender and reproduction are diverse, including among people who are experiencing wanted and/or planned pregnancies, unwanted and/or unplanned pregnancies, and among those who are surrogates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".