Developing Ecosystem Health Indicators in Centro Habana: A Community‐based Approach
Bibliographic record
Abstract
ABSTRACT A set of interventions was undertaken between 1995 and 1999 to improve the quality of life and human health in Cayo Hueso, an inner city community in Central Havana. The municipality and community organizations contacted the agency responsible for public and environmental health in Cuba (INHEM) to evaluate whether these improvements were as effective and efficient as possible, so as to assist in planning further interventions in this and other communities. With the aid of international researchers, an effort was made to strengthen the community's capacity to apply an ecosystem health approach, adapting the analytical framework (DPSEEA: driving force–pressure–state–exposure–effects–action) developed for this purpose by the World Health Organization. A series of workshops and focus groups with community representatives and researchers was conducted in late 1999 and early 2000 to develop appropriate indicators for the analysis. Interventions were grouped into those relating to improved housing, the physical community infrastructure (e.g., water, sewage, street lights), and the socio‐cultural environment (e.g., programs for youths and seniors). The DPSEEA framework was embraced by the community and used to define indicators at the individual, household, and neighborhood levels; the community‐researcher team then collectively elaborated the methodology to obtain the needed information. Data collection is now underway with the process having triggered a series of new partnerships, including other communities (comparison groups) now eager to learn from the Cayo Hueso interventions. With the capacity to apply this approach strengthened, the community is preparing to use the results of the analyses to set new priorities and pursue longer‐term ecosystem health interventions.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".