Sexual Orientation, Mental Health Characteristics, and Self-Care in Professional Psychology Training and Employment
Bibliographic record
Abstract
Although self-care practice is critical for mental healthcare professionals (MHP) to buffer against the impact of stressors and to prevent psychiatric symptoms and functional impairment, effective self-care may be more difficult for those at greater risk of developing mental health conditions due to sexual minority stress. The present study aimed to examine mental health characteristics, engagement in, and value obtained from self-care, and perceptions about training programs’ encouragement of self-care in trainees and professionals of different sexual orientations. Data were collected from MHP trainees and practitioners (n = 547) using an anonymous online survey including measures assessing stress, depression, anxiety, resilience, coping self-efficacy, and self-care strategy usage. Analyses revealed that bisexual individuals reported significantly greater mental health symptoms and self-care strategy usage as compared to heterosexual and gay/lesbian participants. Analyses also revealed lower levels of daily functioning and resilience in bisexual vs. heterosexual or gay/lesbian participants, and that bisexual participants reported obtaining less value from self-care practices than other subgroups. These results suggest that bisexual individuals may experience a unique set of minority stressors which affect their engagement with and benefit from self-care. Implications and suggestions for promoting inclusiveness and positive alterations in self-care engagement in training programs and professional institutions are discussed, including engendering coping self-efficacy through individual skill development, nurturing environments that reinforce self-care, ensuring access to culturally competent services, and promoting engagement in supportive groups and organizations.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".