Characteristics of community savings groups in rural Eastern Uganda: opportunities for improving access to maternal health services
Bibliographic record
Abstract
BACKGROUND: Rural populations in Uganda have limited access to formal financial Institutions, but a growing majority belong to saving groups. These saving groups could have the potential to improve household income and access to health services. OBJECTIVE: To understand organizational characteristics, benefits and challenges, of savings groups in rural Uganda. METHODS: This was a cross-sectional descriptive study that employed both quantitative and qualitative data collection techniques. Data on the characteristics of community-based savings groups (CBSGs) were collected from 247 CBSG leaders in the districts of Kamuli, Kibukuand Pallisa using self-administered open-ended questionnaires. To triangulate the findings, we conducted in-depth interviews with seven CBSG leaders. Descriptive quantitative and content analysis for qualitative data was undertaken respectively. RESULTS: Almost a quarter of the savings groups had 5-14 members and slightly more than half of the saving groups had 15-30 members. Ninety-three percent of the CBSGs indicated electing their management committees democratically to select the group leaders and held meetings at least once a week. Eighty-nine percent of the CBSGs had used metallic boxes to keep their money, while 10% of the CBSGs kept their money using mobile money and banks,respectively. The main reasons for the formation of CBSGs were to increase household income, developing the community and saving for emergencies. The most common challenges associated with CBSG management included high illiteracy (35%) among the leaders,irregular attendance of meetings (22%), and lack of training on management and leadership(19%). The qualitative findings agreed with the quantitative findings and served to triangulate the main results. CONCLUSIONS: Saving groups in Uganda have the basic required structures; however, challenges exist in relation to training and management of the groups and their assets. The government and development partners should work together to provide technical support to the groups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".