Men’s Dropout From Mental Health Services: Results From a Survey of Australian Men Across the Life Span
Bibliographic record
Abstract
While increasing numbers of Australian men are accessing mental health services, the sustainability of their therapy engagement varies significantly, with many men being lost to follow-up. The current study investigated dropout rates in a large community-based male sample to highlight the reasons for, and potential predictors of, men dropping out of mental health care services. Data were drawn from an online survey of 1907 Australian men (aged 16–85; M = 44.1 years) reflecting on their broad experiences in mental health therapy. Participants responded to bespoke items assessing their past dropout experience and reasons for dropping out, the odds of which were modeled in relation to demographics and predictors (e.g., therapist engagement strategies, alignment to traditional masculinity and pre-therapy feelings of optimism, shame, and emasculation). The overall dropout rate from therapy was 44.8% ( n = 855), of which 26.6% ( n = 120) accessed therapy once and did not return. The most common reasons for dropout were lack of connection with the therapist (54.9%) and the sense that therapy lacked progress (20.2%). Younger age, unemployment, self-reported identification with traditional masculinity, the presence of specific therapist engagement strategies, and whether therapy made participants feel emasculated all predicted dropout. Current depressive symptoms and suicidality were also higher amongst dropouts. Therapists should aim to have an honest discussion with all clients about the importance of therapy fit, including the real likelihood of dropout, in order to ensure this does not deter future engagement with professional services.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".