The Virgin Islands National Information Systems for Health: vision, actions, and lessons learned for advancing the national public health agenda
Bibliographic record
Abstract
The British Virgin Islands (BVI) Ministry of Health and Social Development (MOHSD) recently identified the need for an updated strategy to advance the country's vision for Information Systems for Health (IS4H) ("Informed decision-making for better health outcomes"). Since the early 1990s, the MOHSD has recognized the importance of having strong conceptual foundations and mechanisms for its information systems, and the need to strengthen the production and use of good-quality health data to enable fulfillment of the territory's health goals. Therefore, in May 2017, BVI requested technical assistance from the Pan American Health Organization (PAHO) to develop a plan/"road map" for strengthening the MOHSD's stewardship capacity for IS4H. This resulted in a bilateral, country-led collaboration between PAHO and the Ministry to carry out two assessments of BVI's National Information Systems for Health (NISH): 1) a rapid assessment to map NISH policy, to develop a short- and medium-term workplan for strengthening and updating it, and 2) a maturity assessment, using PAHO's IS4H Maturity Model tool, to evaluate the implementation of NISH policy thus far and determine next steps. This article describes 1) the steps taken in this bilateral collaboration to update BVI's NISH policy and fine-tune its IS4H vision, including the development of a national plan/road map, and 2) lessons learned.
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 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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".