What the Cuban context provides health researchers: the feasibility of a longitudinal multi-method study of the impact of housing improvements on health in Havana, Cuba
Bibliographic record
Abstract
BACKGROUND: From extensive participatory research in inner city communities in Central Havana, Cuba, we found housing to be the largest perceived source of health risk. The objective of this study, therefore, was to ascertain the feasibility of conducting a multi-method longitudinal evaluation of the impact of housing improvements on health. METHODS: Meetings with community and governmental stakeholders were held; housing policy documents were reviewed; key informants were interviewed; decisions were made by a collaborative interdisciplinary team regarding measurement instruments for health as well as housing quality; training was conducted for use of new measurement tools; and a 3 month multi-method trial with repeated measures was conducted on individuals in good housing and poor housing in the inner city of Central Havana. Questionnaires were administered at monthly intervals for 3 months to 25 adults living in good housing and 25 in poor housing. RESULTS: Cuba's housing policies made it easy to identify a suitable cohort and control group for possible longitudinal study. Consent to participate was enthusiastically obtained, and no difficulties were encountered in collecting or analysing the data. Housing quality measurements were conducted using validated instruments with minimal difficulties. There was strong community involvement and support for a comprehensive longitudinal study. CONCLUSION: Cuba, although a poor country, has the necessary infrastructural support and capacity to make it an excellent site for health and housing intervention studies.
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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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 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".