001: EHEALTH, TRADE POLICY & SOCIAL ACCOUNTABILITY
Bibliographic record
Abstract
Background Over the last several years, a new generation of multilateral trade agreement negotiations has emerged. With a focus on regulatory harmonization and reductions in non-tariff trade barriers, the Transatlantic Trade Investment Partnership (TTIP), the Trans Pacific Partnership (TPP) and Trade in Services Agreement (TiSA) seek to advance trade liberalization and increase economic growth. These agreements aim to establish a new global framework for trade governance with broad potential implications for research and innovation for health around the world. With more than forty countries representing more than half of the global GDP participating, the potential economic power of these agreements is substantial. On a truly global scale, the TPP, TTIP and TiSA could profoundly affect the future of ehealth. Objectives (1) Describe potential implications of current trade agreement negotiations on ehealth using a social accountability framework; and (2) promote ehealth and social accountability in current trade agreement negotiations. Methods In this context and using a social accountability lense, this presentation analyzes potential implications of current negotiations on several dimensions of ehealth including (1) the provision of health care services (including telemedicine); (2) access to innovation and medical knowledge; and (3) health professional education and training. Result Given the ongoing evolution and expansion of ehealth and its vast potential to advance health equity, it is essential that this next generation of trade agreements protect and promote rather than undermine access to health care services and progress toward universal health coverage. Conclusion To this end, potential strategies and recommendations to promote ehealth and social accountability in trade agreement negotiations will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.165 | 0.037 |
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".