Barriers and facilitators to enrollment and re-enrollment into the community health funds/Tiba Kwa Kadi (CHF/TIKA) in Tanzania: a cross-sectional inquiry on the effects of socio-demographic factors and social marketing strategies
Bibliographic record
Abstract
BACKGROUND: Introduction of a health insurance scheme is one of the ways to enhance access to health care services and to protect individuals from catastrophic health expenditures. Little is known on the influence of socio-demographic and social marketing strategies on enrollment and re-enrollment in the Community Health Fund/Tiba Kwa Kadi (CHF/TIKA) in Tanzania. METHODS: This cross-sectional study employed quantitative methods for data collection between November 2014 and March 2015 in Singida and Shinyanga regions. Relationship between variables was obtained through Chi-square test and multivariate logistic regression. RESULTS: We recruited 496 participants in the study. Majority (92.7%) of participants consented to participate, with 229 (49.8%) and 231 (50.2%) members and non members of CHF/TIKA respectively. Majority (90.9%) were aware of CHF/TIKA. Majority of CHF/TIKA members and non-members (90% and 68.3% respectively) reported health facility-based sensitization as the most common social marketing approach employed to market the CHF/TIKA. The most popular marketing strategies in the country including traditional dances, football games, radio, television, news papers, and mosques/church were reported by few CHF and non CHF members. Multivariate Logistic regression models revealed no significant association between social marketing strategies and enrollment, but only socio-demographics; including marital status (AOR = 2.0, 95% CI 1.1-3.8) and family size (household with ≥ 6 members) (AOR = 1.5, 95% CI 1.0-2.5), were significant factors associated with enrollment/re-enrollment rate. CONCLUSIONS: This study indicated that low level of utilization of available social marketing strategies and socio-demographic factors are the barriers for attracting members to join the schemes. There is a need for applying various social marketing strategies and considering different facilitating and impending socio-demographic factors for the growth and sustainability of the scheme as we move towards universal health coverage.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".