Addressing the move toward universal health in the Caribbean through strengthening the health workforce
Bibliographic record
Abstract
This article describes the human resources for health (HRH) policy and action plan development in Barbados, Grenada, and St. Vincent and the Grenadines, the supporting role of the PAHO/WHO Collaborating Centre on Health Workforce Planning and Research, Dalhousie University, and sub-regional action for supporting continuing country-level HRH strengthening. A policy development process, comprising document/literature review and stakeholder consultations, was used to conduct a situational analysis, which informed the HRH policy and action plan. The policies and action plans centered on HRH priority areas of leadership and governance, HRH planning capacity, strengthening primary health care, optimization of pre- and post-licensure education/training, retention and recruitment, deployment and utilization, inter-sectoral and external partnerships for sustainability, health information systems, and HRH research. A cross-sectional analysis of the findings found that HRH challenges and priority areas were consistent across the countries, resulting in similar policy priority actions that are aligned with the regional lines of action for strengthening HRH for universal access to health and universal health coverage. The results support the value-add in collaborating on a regional level to build capacity for needs-based HRH planning within member countries. The Caribbean-Community (CARICOM), with facilitation by Pan American Health Organization (PAHO) Caribbean Subregional Programme, has established the Human Resources for Health Action Task Force for the Caribbean. The expertise of the Dalhousie University PAHO/WHO Collaborating Centre, provided through the technical assistance, supported the three countries in this important initiative and provides for further opportunities to support PAHO, the Task Force, and countries as they work to achieve their HRH strengthening objectives.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".