Evaluation of collaborative mental health services in Latin America: Theoretical and methodological basis
Bibliographic record
Abstract
OBJECTIVES: Approaches to collaborative mental health care (CMHC) have been implemented in many countries to strengthen the accessibility and delivery of mental health services in primary care. However, there are not well-defined frameworks to evaluate CMHC models. The purpose of this article is to identify, contextualize and discuss relevant health services research approaches, theory, and evaluation models for the development of an appropriate evaluation framework in order to foster effective CMHC in Latin America. METHODS: A comprehensive literature review informed a critical analysis of relevant theories and alternative methods to be considered in the development of the framework. RESULTS: Specific health services research frameworks are discussed in the context of evaluating CMHC. Two theoretical perspectives - collaboration theory and systems theory - and three evaluation models- realistic, developmental and collaborative - are analyzed in terms of their relevance. Methodological implications are identified. CONCLUSION: An appropriate evaluation framework for CMHC in Latin America needs to reflect theoretical and contextual considerations and relevant evaluation approaches and methods, including key dimensions and attributes/variables, core indicators, and recommendations for implementation.
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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 | 0.176 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".