Health-risk Perception in the Inner City Community of Centro Habana, Cuba
Bibliographic record
Abstract
Perceptions of health risks were surveyed in the inner city of Centro Habana, Cuba. A questionnaire developed by community leaders and experts was administered to 348 residents to determine the level of perceived risk for each of 41 risk items. Ecologic-level data on morbidity, mortality, and environmental indicators were also gathered. Using factor analysis to reduce the dimensionality of the data, five factor groupings accounted for 60% of the variance, as follows: social environment (40.8%); infectious agents and other health-risk factors of immediate concern (6.1%); lifestyle risks (4. 9%); environmental sanitation (4.1%); and living conditions (3.3%). A relationship between the perception of risk and the ecologic data was found, with inconsistencies largely attributable to factors known to influence risk perception. The greatest concern identified throughout the municipality was housing conditions, highest in the neighborhood that had already begun to address this problem. The analysis was useful in planning targeted health promotion campaigns and prioritizing further interventions. Repeat evaluation of risk perception will be conducted following the completion of interventions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".