<scp>Minority</scp> stress and structural stigma predict well‐being in European <scp>LGBTQ</scp>+ parents
Bibliographic record
Abstract
Abstract Objective This study tested whether exposure to minority stress and structural stigma across multiple levels of the family system were associated with two indicators of well‐being (life satisfaction, depressive symptoms) in LGBTQ+ parents across 19 European countries. Background Minority stress (i.e., identity‐based stress resulting from systemic oppression) and structural stigma (i.e., hostile legal environments, prejudicial social attitudes) are heterogeneous, yet well‐documented risk factors of reduced well‐being within LGBTQ+ populations. However, a comprehensive assessment stratifying both concepts across multiple levels of the family system (i.e., the individual, couple, and family level) is lacking for LGBTQ+ parents. Method Using data from the EU LGBTI Survey 2019, a sample of 3808 LGBTQ+ parents from 19 European countries was analyzed. Associations between self‐reported minority stress indicators, objective structural stigma indicators, sociodemographic predictors, and well‐being were tested using non‐linear, machine learning‐based techniques (gradient boosted decision tree models). Results Supporting preregistered hypotheses, exposure to individual‐level minority stress and individual‐ and family‐level structural stigma predicted life satisfaction and depressive symptoms. Couple‐level minority stress predicted life satisfaction, but not depressive symptoms, and family‐level minority stress predicted neither. Trans parents and those facing economic burdens were particularly vulnerable to low well‐being. Conclusions Exposure to minority stress and structural stigma, particularly within highly stigmatizing regions, are risk factors for LGBTQ+ parents' well‐being. Future research should examine the role of family‐level minority stress using validated measures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".