Mental Health, Coping, Resilience, and Community in Plurisexual Emerging Adults
Bibliographic record
Abstract
Despite cultural shifts in the acceptance of sexual minority individuals, plurisexual people continue to face mental health disparities in comparison to their monosexual counterparts. This study investigated how stressors (e.g., identity uncertainty, internalized homonegativity, experiences of microaggression), and resources (e.g., resilience, social support, connectedness to LGBTQIA2S+ communities) relate to psychological well-being and distress in plurisexual emerging adults. It also explored how this population describes and labels their sexual identities. Participants completed an online survey assessing the above-listed variables, as well as measures related to sexual identity, fluidity, and coping. All 774 participants were between the ages of 18 and 29, experienced attraction to more than one gender, and were living in Canada or the USA. Results indicated high levels of anxiety and depression in the sample. Minority stress was predictive of poorer mental health outcomes, while social support and resilience served as protective factors. Implications for counselling interventions are discussed, as well as the importance of education and training for clinicians to address the specific needs of this population.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".