Information and communication technology (ICT) and eHealth policy in Latin America and the Caribbean: a review of national policies and assessment of socioeconomic context.
Bibliographic record
Abstract
OBJECTIVE: To examine the availability of national information and communication technology (ICT) or eHealth policies produced by countries in Latin America and the Caribbean (LAC), and to determine the influence of a country's socioeconomic context on the existence of these policies. METHODS: Documents describing a national ICT or eHealth policy in any of the 33 countries belonging to the LAC region as listed by the United Nations were identified from three data sources: academic databases; the Google search engine; and government agencies and representatives. The relationship between the existence of a policy and national socioeconomic indicators was also investigated. RESULTS: There has been some progress in the establishment of ICT and eHealth policies in the LAC region. The most useful methods for identifying the policies were 1) use of the Google search engine and 2) contact with Pan American Health Organization (PAHO) country representatives. The countries that have developed a national ICT policy seem to be more likely to have a national eHealth policy in place. There was no statistical significant association between the existence of a policy and a country's socioeconomic context. CONCLUSIONS: Governments need to make stronger efforts to raise awareness about existing and planned ICT and eHealth policies, not only to facilitate ease of use and communication with their stakeholders, but also to promote collaborative international efforts. In addition, a better understanding of the effect of economic variables on the role that ICTs play in health sector reform efforts will help shape the vision of what can be achieved.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".