Prioritizing LGBTQ Clients’ Mental Health: How Christian Therapists Resolve Internal Conflicts to Remain Ethical
Bibliographic record
Abstract
This study investigated how Christian therapists are able to resolve any internal conflicts that arise when working with lesbian, gay, bisexual, transgender, and queer (LGBTQ) clients. The study focused on the participants’ internal processes when working with LGBTQ clients, highlighting individual experiences and themes. The study revealed that the more open-minded and self-aware participants were, the more likely they were to have intentional practices to resolve internal conflicts. It also revealed that education, particularly at seminaries, is not adequately preparing therapists to resolve internal conflicts or work with gender and sexual minorities. Ultimately, this research study underscored the importance of therapists being aware of their own beliefs and the impact they may have, as well as being educated on LGBTQ issues, microaggressions, and how to work with gender and sexual minority clients without discrimination and judgment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".