Strengthening Noncommunicable Disease Research Capacity and Chronic Disease Outcomes in Low- and Middle-Income Countries in South Asia: Implementation and Evaluation of the ASCEND Program
Bibliographic record
Abstract
This article describes the design, outcomes, challenges, and lessons learned from the ASian Collaboration for Excellence in Non-Communicable Disease (ASCEND) program, implemented between 2011 and 2015 in India, Sri Lanka, and Malaysia. The program involved a blended-delivery model, incorporating online and face-to-face training, mentoring, and supervision of trainees' research projects. Evaluation data were collected at baseline, 6, 12, 18, and 24 months. Intended outcomes, lessons, and challenges were summarized using a logic model. During the program period, 48 participants were trained over 2 cohorts in June 2011 and 2012. The trainees published 83 peer-reviewed articles between 2011 and 2015. Additionally, 154 presentations were given by trainees at national and international conferences. Underutilization of the online learning management system was an important challenge. Utilizing a combination of intensive face-to-face and online learning and mentoring of early career researchers in low- and middle-income countries has great potential to enhance the research capacity, performance, and outputs.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".