Emerging eHealth Directions in the Philippines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it