Healing through community connection? Modeling links between attachment avoidance, connectedness to the LGBTQ+ community, and internalized heterosexism.
Bibliographic record
Abstract
Sexual minorities high in attachment avoidance (i.e., discomfort with closeness) and attachment anxiety (i.e., fear of abandonment) tend to report greater internalized heterosexism. Yet, the causes of this link have not been fully explored. Some propose that insecure attachment schemas may make it difficult to form the types of social connections that can help alleviate internalized stigma (and vice versa: internalized heterosexism might make one avoid the types of relationships that would foster secure attachment). This study used structural equation modeling to test whether reduced connection to the LGBTQ+ community could help explain the link between insecure attachment and internalized heterosexism. Study 1 (n = 480) explored links between attachment avoidance, attachment anxiety, community connectedness and internalized heterosexism. Higher avoidance predicted lower connection which, in turn, predicted higher internalized heterosexism. Attachment avoidance's association with internalized heterosexism was fully explained by an indirect effect through connectedness. Conversely, attachment anxiety did not predict connectedness or internalized heterosexism. Study 2 (n = 447) replicated these findings. These results suggest low connectedness might help explain the association between attachment insecurity and internalized heterosexism, though this path might be specific to attachment avoidance. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".