Impact of the Health Gym Program on hospital admissions for stroke in the state of Pernambuco, Brazil
Bibliographic record
Abstract
This study aimed to evaluate the impact of the Health Gym Program (HGP) on hospital admissions for stroke in the state of Pernambuco, Brazil. This policy impact evaluation used a quasi-experimental approach consisting of a difference-in-differences estimator, weighted by propensity score matching to deal with potential confounding variables. The study comprised socioeconomic, demographic, and epidemiological data from official Brazilian databases from 2010 to 2019. The treatment group was composed of the 134 municipalities that implemented the HGP since 2011. The 51 municipalities that did not were allocated to the comparison group. The nearest neighbor algorithm (N5) was used to pair treatment and comparison group municipalities and create the weights to evaluate the average treatment effect on the treated (ATT) in the difference-in-differences estimator. In 2010, 2,771 people were hospitalized for stroke (0.51% of all hospitalizations) and in 2019, 11,542 (2%). Municipalities that implemented the HGP had 18.37% fewer hospitalizations than their counterparts in the comparison group. The program’s impact in reducing hospitalization rates was incrementally greater among men (ATT: -0.1932) and those aged 71 to 80 years (ATT: -0.1911). All results were statistically significant at the 5% level. The HGP reduced hospitalization for stroke in several population groups, but primarily in those whose underlying prevalence of stroke is highest, reinforcing the importance of public investments in health promotion policies designed to encourage lifestyle changes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".