"I tell you, getting data for this is hell"–Exploring the use of evidence for noncommunicable disease policies in Ghana
Bibliographic record
Abstract
After several years of over concentration on communicable diseases, Ghana has finally made notable strides in the prevention of NCDs by introducing key policies and programmes. Evident shows that there is limited NCD-related data on mortality and risk factors to inform NCD policy, planning, and implementation in Ghana. We explored the evidence base for noncommunicable disease policies in Ghana. A qualitative approach was adopted using key informant interviews and documents as data sources. An adaptation of the framework method for analysing qualitative data by Gale and colleagues' (2013) was used to analyse data. Our findings show that effort has been made in terms of institutions and systems to provide evidence for the policy process with the creation of the Centre for Health Information Management and the District Health Information Management System. Although there is overreliance on routine facility data, policies have also been framed using surveys, burden of disease estimates, monitoring reports, and systematic reviews. There is little emphasis on content analysis, key informant interviews, case studies, and implementation science techniques in the policy process of Ghana. Inadequate and poor data quality are key challenges that confront policymakers. Ghana has improved its information infrastructure but access to quality noncommunicable disease data remains a daunting challenge. A broader framework for the integration of different sources of data such as verbal autopsies and natural experiments is needed while strengthening existing systems. This, however, requires greater investments in personnel and logistics at national and district levels.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".