Men’s preferences for therapist gender: Predictors and impact on satisfaction with therapy
Bibliographic record
Abstract
Little empirical data exists regarding men’s preferences for therapist gender, including what predicts these preferences, and the impact they may have on satisfaction with care. To address this, data were drawn from an online survey of Australian men (n = 2002; aged 16–85; M = 43.8 years) reflecting on their preferences for and experiences of mental health treatment. Participants responded to items assessing demographics alongside their preference for therapist gender, reason for this preference and items on masculinity and treatment satisfaction, which were entered into a predictive model. Findings indicated that the majority (60.5%) of respondents did not indicate a preference, while equal proportions preferred male (19.1%) and female therapists (20.4%). Undergraduate-educated, non-heterosexual, and more masculine-identifying men were all more likely to prefer a male therapist. Severely depressed men preferred a female therapist. Finally, seeing a therapist who matched one’s gender preference was a significant predictor of satisfaction with therapy, while feeling less manly in attending therapy mediated this relationship. While the majority of men reported no gender preference for their therapist, for those who do, the underpinnings and implications warrant consideration and discussion. Limitations and clinical and research implications are discussed.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".