Global mental health: transformative capacity building in Nicaragua
Bibliographic record
Abstract
BACKGROUND: Mental health is increasingly recognised as integral to good public health, but this area continues to lack sufficient planning, resources, and global strategy. It is a pressing concern in Latin America, where social determinants of health aggravate existing inequities in access to health services. Nicaragua faces serious mental health needs and challenges. One key strategy for addressing gaps in mental health services is building capacity at the primary healthcare and system levels. OBJECTIVE: Using the framework of best practice literature, this article analyses the four-year collaborative process between the National Autonomous University of Nicaragua in León (UNAN-León) and the Centre for Addiction and Mental Health (CAMH) in Canada, which is aimed at improving mental healthcare in Nicaragua. DESIGN: Based on a critical analysis of evaluation reports, key documents, and discussion among partners, the central steps of the collaboration are analysed and main successes and challenges identified. RESULTS: A participatory needs assessment identified local strengths and weaknesses, expected outcomes regarding competencies, and possible methodologies and recommendations for the development of a comprehensive capacity-building programme. The partners delivered two international workshops on mental health and addiction with an emphasis on primary healthcare. More recently, an innovative Diploma and Master programme was launched to foster interprofessional leadership and effective action to address mental health and addiction needs. Collaborative activities have taken place in Nicaragua and Canada. DISCUSSION: To date, international collaboration between Nicaragua and CAMH has been successful in achieving the jointly defined goals. The process has led to mutual knowledge sharing, strong networking, and extensive educational opportunities. Evidence of effective and respectful global health capacity building is provided. Lessons learned and implications for global health action are identified and discussed.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| 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".