Promoting health in response to global tourism expansion in Cuba
Bibliographic record
Abstract
The ability of communities to respond to the pressures of globalization is an important determinant of community health. Tourism is a rapidly growing industry and there is an increasing concern about its health impact on local communities. Nonetheless, little research has been conducted to identify potential mitigating measures. We therefore took advantage of the 'natural experiment' provided by the expansion of tourism in Cuba, and conducted four focus groups and key informants interviews in each of two coastal communities. Participants expressed concerns about psycho-social impacts as well as occupational and environmental concerns, and both infectious and chronic diseases. A wide array of programs that had been developed to mitigate potential negative were described. Some of the programs were national in scope and others were locally developed. The programs particularly targeted youth as the most vulnerable population at risk of addictions and sexually transmitted infections. Occupational health concerns for workers in the tourism sector were also addressed, with many of the measures implemented protecting tourists as well. The health promotion and various other participatory action initiatives implemented showed a strong commitment to address the impacts of tourism and also contributed to building capacity in the two communities. Although longitudinal studies are needed to assess the sustainability of these programs and to evaluate their long-term impact in protecting health, other communities can learn from the initiatives taken.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".