Effectiveness of community health agents' actions in situations of social vulnerability
Bibliographic record
Abstract
Evaluation is purposeful activity examining multiple, diverse realities [1] that affect the implementation of social interventions and their management [2]. As political activity, evaluation involves partnerships among managers, stakeholders and internal and external evaluators. These partners review common interests and concerns to modify policies and modi operandi, and ultimately, to influence human life [3]. Evaluation is particularly sensitive to social problems and expectations; it documents their features, incidence and prevalence [2]. This article reports the quanti-qualitative results of an in-service effectiveness evaluation of interventions to reduce health risks for socially vulnerable people by community health agents (CHAs) (Agentes Comunitarios de Saude) in Brazil. CHAs are key personnel within the nationwide community health agent program (CHAP), created in 1991, that operates within Brazil’s Family Health Strategy (FHS). CHAP considers social inclusion through health education and promotion, a cornerstone of collective health. Most CHAs are from the communities they serve. This article documents some crucial features of CHAs’ work in dangerous neighborhoods previously inaccessible to health professionals (HPs). Knowledge about these residents’ health needs, challenges and difficulties due to their social vulnerability may not have reached health care providers.
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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 | 0.073 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".