Exposure to mass media chronic health campaign messages and the uptake of non-communicable disease screening in Ghana
Bibliographic record
Abstract
The main goal of this study was to examine the relationship between exposure to mass media health campaign massages and the uptake of non-communicable diseases (NCDs) screening services in Ghana and whether this relationship differs by place of residence. Available evidence suggests a general low uptake of NCDs screening in developing country settings. Unfortunately, many NCDs evolve very slowly and are consequently difficult to detect early especially in situations where people do not screen regularly and in settings where awareness is low. In this study, we contribute to understanding the potential role of the media in scaling up NCDs screening in developing countries. We fitted multivariate logistic regression models to a sample of 1337 individual surveys which were collected at the neighborhood level in three Ghanaian cities. Overall, the results show that exposure to mass media chronic NCD health campaign messages was significantly associated with increased likelihood of screening for NCDs. The results further highlight neighborhood-level disparities in the uptake of NCDs screening services as residents of low-income and deprived neighborhoods were significantly less likely to report being screened for NCDs. Other factors including social capital, knowledge about the causes of NCDs and self-rated health predicted the likelihood of chronic NCDs screening. The results demonstrate mass media can be an important tool for scaling up NCDs screening services in Ghana and similar contexts where awareness might be low. However, place-based disparities need to be addressed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".