No Man Left Behind: Effectively Engaging Male Military Veterans in Counseling
Bibliographic record
Abstract
Ex-military men have emerged as a vulnerable subgroup for mental illness amid long-standing trends signaling men's reticence to seek professional help. Less explored is how men engage or disengage when they actually do enter helping programs. Contrasting decades of quantitative research pairing masculine ideology with low help seeking (i.e., describing the problem), this article draws on qualitative data to distill factors that help men become engaged and committed to counseling (i.e., identifying solutions). Shared is an evaluation of a treatment program with high success rates and virtually no dropouts-a unique occurrence in men's counseling. Enhanced Critical Incident Technique data suggest that helping men feel competent and free from judgment in the company of down-to-earth peers and genuine practitioners are instrumental in helping men draw benefit from counseling. While appealing to male gender roles may be critical in recruiting men to counseling, men can transition to embrace virtues (i.e., that might be shared by men and women alike) and universal human needs as counseling progresses.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".