Determinants of chronic non-communicable disease screening among adults in Ghana
Bibliographic record
Abstract
The pervasiveness and growing concern of non-communicable diseases (NCDs) in developing countries, which were considered not in the distant historical past, as diseases of only the affluent and of developed countries, is no longer in doubt. The proportion of deaths attributable to NCDs today includes figures never witnessed in any previous historical period. Delayed screening is one of the drivers of this worrying trend. The objective of this study, therefore, was to examine the determinants of NCD screening in Ghana. We fitted logistic regression models to a sample of 1342 individuals who successfully completed a cross-sectional survey across three cities in Ghana. Results show that factors such as knowledge on the causes of NCDs, health insurance, high neighbourhood social capital, poor self-rated health, and exposure to NCDs campaign messages significantly predicted an increased likelihood of NCDs screening. However, socio-cultural beliefs such as developing NCDs being the will of God or the enemy being able to spiritually inflict an individual with NCDs were found to be associated with a lower likelihood of screening for NCDs. Beyond the traditional determinants of health, this study expands the analytical gaze and frontiers of the determinants of NCDs in Ghana through the lens of socio-cultural belief systems. While improving access to healthcare, exposure and knowledge through educational programs as well as building community social capital might be important in scaling up NCD screening, dismantling untenable beliefs and misconceptions is similarly crucial.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".