Examining the association between exposure to mass media and health insurance enrolment in Ghana
Bibliographic record
Abstract
Although previous studies have explored the National Health Insurance Scheme (NHIS) in Ghana, very little attention is paid to the influence of mass media exposure on NHIS enrolment. Yet, understanding this linkage is important, particularly due to the critical role of mass media in disseminating health information and shaping people's health perceptions and choices. Using data from the 2014 Ghana Demographic and Health Survey, we employed logistic regression analysis to understand the relationship between NHIS enrolment and exposure to print media, radio, and television. Our findings indicate that women with more exposure to radio (OR = 1.23, P < 0.01) and television (OR = 1.24, P < 0.01) were more likely to enroll in the NHIS than those with no exposure. For men, more exposure to print media was associated with higher odds of enrolling in the NHIS (OR = 1.41, P < 0.01). In conclusion, all 3 types of media may be helpful in promoting NHIS enrolment in Ghana. However, given that the relationship between media exposure and enrolment in the NHIS was gendered, we recommend that policymakers should pay attention to these dynamics to ensure effective targeting in NHIS media campaigns for increased enrolment into the scheme.
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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 | 0.007 |
| 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) | 0.005 | 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".