Emerging eHealth Directions in the Philippines
Bibliographic record
Abstract
OBJECTIVES: This paper aims to provide an overview of research and education initiatives in the Philippines. Moreover, it outlines the various agencies and organizations that spearhead the eHealth projects. METHODS: The researchers utilized internet-based review of literature, key informant interviews and proceedings from two eHealth conferences among Filipino researchers in 2011 organized by the authors. RESULTS: eHealth capacities in the areas of research, education and service have progressed dramatically in the last four decades as a result of improved access to information and communication technology. The National Unified Health Research Agenda initiatives have been led largely by higher educational institutions and organizations specializing in eHealth. Educational reforms have been seen with the establishment of the Masters of Science in Health Informatics, infusion of Nursing Informatics into the nursing undergraduate curriculum and offering of short courses on eHealth. Service- oriented organizations and innovations have also been formulated to meet the needs of the practitioners as information and communication technologies are embedded into the healthcare delivery system. CONCLUSIONS: Experts, researchers, practitioners and enthusiasts have successfully promoted awareness and uplifted the standards in the practice of eHealth in research, education and service. However, three main areas of improvement need to be given priority: (1) Policy and standards creation, (2) capability building and (3) multi-sectoral collaborations.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".