Stigma and self-stigma among women within the context of the german “zero alcohol during pregnancy” recommendation: A qualitative analysis of online forums and blogs
Bibliographic record
Abstract
BACKGROUND: In many countries, including Germany, it is recommended to abstain from alcohol during pregnancy to avoid harm to the baby. In this qualitative research study, analysis of online forums was conducted to explore women's perception of the German "zero alcohol during pregnancy" recommendation with regard to stigma and self-stigma. METHODS: We used a grounded theory approach to analyze online forum discussions on alcohol use during pregnancy. Data consisted of 9 discussion threads from 5 different forums and blogs involving 115 participants in total. We used key concepts developed during analysis and the theory of stigma to interpret the posts. RESULTS: We identified five key themes: (1) Low alcohol health literacy as a breeding ground for stigmatization; (2) The widespread assumption that maternal abstinence is a prerequisite for being considered a "good mother"; (3) Interpersonal role conflicts and a guilty conscience as a result of stigmatization or self-stigmatization; (4) Paying little attention to the role of psychosocial factors in alcohol consumption, especially regarding partner responsibility during pregnancy.; (5) Understanding the "zero alcohol during pregnancy" recommendation as a complete ban, associated with loss of autonomy. CONCLUSION: The current method of communicating the "zero alcohol during pregnancy" recommendation may have unintended consequences. Specifically, misconceptions about the harm associated with low alcohol consumption and setting high expectations of motherhood are factors that can contribute to stigma or self-stigma and potentially undermine self-efficacy, help-seeking behavior, and overcoming the barriers to alcohol health literacy.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".