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 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 | 0.194 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".