Engaging Men in Psychological Treatment: A Scoping Review
Bibliographic record
Abstract
Tailoring psychological treatments to men's specific needs has been a topic of concern for decades given evidence that many men are reticent to seek professional health care. However, existing literature providing clinical recommendations for engaging men in psychological treatments is diffuse. The aim of this scoping review was to provide a comprehensive summary of recommendations for how to engage men in psychological treatment. Four electronic databases (MEDLINE, PubMed, CINAHL, PsycINFO) were searched for articles published between 2000 and 2017. Titles and abstracts were reviewed; data extracted and synthesized thematically. Of 3,627 citations identified, 46 met the inclusion criteria. Thirty articles (65%) were reviews or commentaries; 23 (50%) provided broad recommendations for working with all men. Findings indicate providing male-appropriate psychological treatment requires clinicians to consider the impact of masculine socialization on their client and themselves, and how gender norms may impact clinical engagement and outcomes. Existing literature also emphasized specific process micro-skills (e.g., self-disclosure, normalizing), language adaption (e.g., male-oriented metaphors) and treatment styles most engaging for men (e.g., collaborative, transparent, action-oriented, goal-focused). Presented are clinical recommendations for how to engage men in psychological treatments including paying attention to tapping the strengths of multiple masculinities coexisting within and across men. Our review suggests more empirically informed tailored interventions are needed, along with formal program evaluations to advance the evidence base.
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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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".