Theme 2. Telehealth and Communication Technologies in Health: Summary and Action Plan
Bibliographic record
Abstract
INTRODUCTION: Rapid innovations and improvements in communication technologies have opened many new channels for health education and delivery, as well as disaster management. Theme 2 examined the role and applicability of these technologies to Disaster Medicine and Management and the various issues involved in their use. METHODS: Details of the methods used are provided in the introductory paper. The chairs moderated all presentations and produced a summary that was presented to an assembly of all of the delegates. The chairs then presided over a workshop that resulted in the generation of a set Action Plans that then were reported to the collective group of all delegates. RESULTS: Main points developed during the presentations and discussion included harnessing convergence, seeking interoperability, building partnerships and making it appropriate. This group identified four Principles of Action underlying its plan: (1) investigate possibilities, (2) identify stakeholders, (3) invite participation, and (4) involve discussants in activities. DISCUSSION: Action plans were categorized into three areas that included "thinking globally, acting regionally", forming a telehealth advisory group, and increasing corporate partnerships. CONCLUSIONS: Technology is opening many opportunities that have applications in disaster management. To optimize benefits, goals and standards must be agreed upon and implemented.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".