“Put your personality into the call”: A qualitative interview study illuminating strategies for improving men’s engagement on crisis helplines
Bibliographic record
Abstract
BACKGROUND: Crisis telephone helplines are an integral part of community suicide prevention. Despite high male suicide rates, men's experiences of these services are poorly understood. The current study explored men's perspectives of their interactions with helpline counsellors to understand how their engagement on helplines can be enhanced. METHOD: Sixteen men (19-71 years) who had previously used a mental health or crisis helpline in Australia completed individual semi-structured interviews about their experiences. Data were analysed using interpretive descriptive methodologies. RESULTS: Two themes derived from the data related to how men engaged with counsellors on helpline services. First, men emphasized the importance of helpline counsellors creating and maintaining an authentic connection across the call, providing suggestions for strategies to secure connection. Second, men discussed how counsellors can facilitate outcomes through offering space for their narratives and aiding in referrals to other support services when required. CONCLUSIONS: Findings highlight the value of crisis helplines for men's suicide prevention services while identifying target areas to improve engagement. We discuss implications for the findings including suggestions for gender-sensitive care within crisis helplines.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".