Developing and Testing an Evaluation Framework for Collaborative Mental Health Services in Primary Care Systems in Latin America
Bibliographic record
Abstract
To develop and pilot-test a feasible and meaningful evaluation framework to support the ongoing improvement and performance measurement of services and systems in Latin America regarding Collaborative Mental health Care (CMHC). This mixed methods study, guided by a developmental evaluation approach, included: (1) a critical review of the literature; (2) an environmental scan at three selected health networks in Mexico, Nicaragua and Chile; (3) a Delphi group with experts; (4) a final consultation in the three sites; and (5) a pilot-test of the framework. A comprehensive evaluation framework was developed and successfully piloted. It considers five levels, 28 dimensions and 40 domains, as well as examples of indicators and an implementation plan. This evaluation framework represents an important effort to foster accountability and quality regarding CMHC in Latin America. Recommendations to build upon current capacity and to effectively address the existing implementation challenges are further discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.215 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".