Charting the Course to Universal Health in the Americas: Cristian Morales PhD, PAHO/WHO Representative in Cuba.
Bibliographic record
Abstract
After leaving Chile during the Pinochet era, Dr Morales studied economics, health administration and international health at the University of Montreal. But his baptism in the field came in Haiti, where he was first PAHO advisor to the health ministry, and then for five years was responsible for human resources and health economics in the PAHO offices in the capital of Port-au-Prince. He was at his post during the flooding in Gonaïves, five hurricanes, the 2010 earthquake and the ensuing cholera epidemic-doubtless the most dramatic and complex times for the country's health in recent history. Before becoming the PAHO/WHO Representative in Cuba in 2015, he was Regional Advisor in Financing and Health Economics based in Washington, DC. In that role, he plunged into the often thorny debates about just how far governments of the Americas were willing to go towards achieving universal health-universal coverage plus universal access. The result was a historic resolution passed in late 2014 by PAHO's Directing Council (CD53.R14 Strategy for Universal Access to Health and Universal Health Coverage). Dr Morales talks about the process, the outcomes… and the road ahead.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".