Strategic factors for the sustainability of a health intervention at municipal level of Brazil
Bibliographic record
Abstract
The present study aims to describe the evolution of an intervention, using a methodology that adopts the critical event as the unit of analysis, and to identify strategic factors that facilitate the continuation of the interventions. Six critical events were identified: dispute care models for health; area of advice: dispute field; change policy; break of interorganizational relations; lack of physical structure and turnover of staff; difficulty in organizing practices in the work process. these are developed into strategic factors: enabling network of allies; meetings and educational activities/building capacity; benefits perceived by community members; mobilization of key actors; intervention's compatibility with the government's vision; restoration of interrelationship; and stability of the workforce. These strategic factors form a group of interrelated conditions that provide the strengthened linkages between elements in the intervention, supporting the hypothesis that they collaborate for the sustainability of the interventions in health. Tracking down the transformations of an intervention set by the critical events, it was verified that these factors performed a protective role at times of changes in the intervention process.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".