Building primary health care capacity to address addiction and mental health inequities: lessons from Latin America and the Caribbean
Bibliographic record
Abstract
Purpose Since 2002, the Centre for Addiction and Mental Health in Ontario, Canada, has been working closely with partners in Latin America and the Caribbean (LAC) to implement mental health capacity‐building focused on primary health care. From an equity perspective, this article seeks to critically analyze the process and key results of this capacity‐building effort and to identify various implications for the future. Design/methodology/approach This analysis of capacity‐building approaches is based on a critical review of existing documents such as needs assessments and evaluation reports, as well as reflective discussion. Previous health equity literature is used as a framework for analysis. Findings More than 1,000 professionals have been engaged in various kinds of training in Chile, Peru, Brazil, Nicaragua, Mexico, and Trinidad and Tobago. These capacity‐building initiatives have had an impact on primary health care from both an equity and systems perspective because participants were engaged at all stages of the process and implementation lessons incorporated into the final efforts. Stigma was also reduced through the collaborations. Originality/value Using concrete examples of capacity‐building in mental primary healthcare in LAC, as well as evidence gathered from the literature, this article demonstrates how primary healthcare can play a strong role in addressing health equity and human rights protection for people with mental health and/or substance abuse problems.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".