Opportunities and challenges to non-communicable disease (NCD) research and training in Pakistan: a qualitative study from Pakistan
Bibliographic record
Abstract
INTRODUCTION: Most of the global non-communicable disease (NCD)-related death burden is borne by low and middle-income countries (LMICs). In LMICs like Pakistan, however, a major gap in responding to NCDs is a lack of high-quality research leading to policy development and implementation of NCDs. To assess institutional opportunities and constraints to NCD research and training we conducted a situational analysis for NCD research and training at Aga Khan University Pakistan. METHODS: We conducted a descriptive exploratory study using grounded theory as a qualitative approach: semistructured interviews of 16 NCD stakeholders (three excluded) and two focus group discussions with postgraduate and undergraduate trainees were conducted. A simple thematic analysis was done where themes were identified, and then recurring ideas were critically placed in their specific themes and refined based on the consensus of the investigators. RESULTS: The major themes derived were priority research areas in NCDs; methods to improve NCD research integration; barriers to NCD research in LMICs like Pakistan; design of NCD research programme and career paths; and NCD prevention at mass level, policy and link to the government. In general, participants opined that while there was an appetite for NCD research and training, but few high-quality research training programmes in NCDs existed, such programmes needed to be established. The ideal NCD research and training programmes would have in-built protected time, career guidance and dedicated mentorship. Most participants identified cardiovascular diseases as a priority thematic area and health information technology and data science as key methodological approaches to be introduced into research training. CONCLUSION: We conclude from this qualitative study on NCD research and training that high-quality research training programmes for NCDs are rare. Such programmes need to be established with in-built protected time, career guidance and mentorship for the trainees to improve their research capacity in Pakistan.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".