“I Fear Coming Out as an Atheist More than as Queer”: Exploring the Presence and Nature of Non-Religious Microaggressions in U.S. Mental Health Therapy
Bibliographic record
Abstract
While extant research has centered religious microaggressions in therapeutic contexts, there has been little research on non-religious populations in psychotherapy, although evidence suggests the presence of negative therapeutic encounters for this minoritized population. The purpose of the current study was to both quantify and explore the prevalence of non-religious microaggressions in therapy and to identify their impact on the therapeutic working alliance and therapeutic continuity. Utilizing a mixed-methods approach, participants (N = 120) were asked to report on religious/spiritual (R/S) conversational contexts and behaviors in their most recent therapeutic relationships. Quantitative analyses revealed that almost half of our participants experienced non-religious microaggressions from their counselor (e.g., assumptions about client religiosity, endorsing stereotypes, etc.)—which significantly impacted the continuation of therapy as mediated by therapeutic working alliance. Qualitative conceptual analyses showed a significant presence of counselor avoidance of R/S topics and, when discussed, negative client experiences. Suggestions for the importance of standardizing R/S clinical training are discussed alongside the unique nature of being non-religious within a predominantly religious country.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".