“I Have Kept it a Secret My Whole Life”: Sexual and Gender Minority Identity Concealment, Secret Keeping, and Minority Stress
Bibliographic record
Abstract
People who identify as Sexual or Gender Minorities (SGMs) tend to report more symptoms of depression and anxiety relative to those who identify as cisgender heterosexuals (non-SGMs).The primary objective of this study was to assess factors that contribute to elevated rates of anxiety and depression among SGMs.According to the Minority Stress Model, these elevated rates are due to exposure to distal stressors that are disproportionately experienced by SGMs relative to non-SGMs and the proximal manifestations of such exposure.From an online survey sample of 476 SGM and 171 non-SGM adults collected on Prolific.co,I assessed distal stressors and their proximal manifestations, secret-keeping behaviours, depression, and anxiety symptoms.Results confirm that (a) SGMs report significantly more symptoms of anxiety and depression than non-SGMs, and that (b) proximal (e.g., internalized stigma) and distal forms of stress (e.g., discrimination, victimization) identified in Meyer's model significantly predict these symptoms in the SGM sample.Importantly, both proximal and distal stressor experiences positively predict the extent to which individuals choose to conceal their sexual/gender identity, which in turn predicts symptomatology.A further finding of the current study is that SGMs reported keeping secrets across more categories of secrets and secret-related preoccupation than did non-SGMs.An implication of these findings is that identity concealment may play a more pivotal role in the mechanisms by which environmental stressors lead to anxiety and depression symptoms than previously established.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".