Barriers to the use of mental health services amongst men in Nigeria and the potential of digital mental health support
Bibliographic record
Abstract
Objective Mental health problems are increasing. Nonetheless, the uptake of professional mental health support remains very low in Nigeria and other African countries, especially among men. This study explored the potential of digital mental health support in eliminating barriers to professional mental health services amongst men in Nigeria.Method Qualitative data were collected between July-August 2022 using an In-depth Interview (IDI) Guide with 24 men aged 18–38. NVivo 12 was employed to assist with the analysis.Results Digital mental health support has the potential to improve acceptance and uptake of professional mental health support services, reduce deterrent factors such as stigma, issues with confidentiality and trust, cost and availability. The use of digital support may also mitigate the nature of masculinity which deters some men from asking for help. Regulations around providing mental health support may improve men’s confidence in seeking professional mental health support.Conclusion Findings highlight the acceptability of digital mental health support for men, and the need for clinical practice regulations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".