Associations between Socioeconomic Factors and Social Capital amongst Child Caregivers in Eastern Uganda
Bibliographic record
Abstract
The main objective of the study was to assess the socioeconomic and demographic determinants of social capital amongst child caregivers in the Iganga and Mayuge Health and Demographic Surveillance Site Eastern Uganda. Logistic regression models were used to analyze associations between 4 social capital dimensions and three socio-demographic parameters among child caregivers in (n=2, 582). The study findings highlights gender-associated differences of perceived social capital implies need for a different approach between men and women when designing interventions that modulate or work through social capital. Female caregivers, living in high quintile households were less likely to perceive high social capital ¨C trust OR 0.67; 95% CI 0.46-0.97; instrumental support OR 0.74; 95% CI 0.58-0.94; informational support (OR 0.57; 95% CI 0.43-0.75). Male caregivers, living in a high quintile household were less likely to perceive high levels of reciprocity (OR 0.64; 95% CI 0.44-0.92). Male caregivers older than 30 years old were more likely to perceive high levels of informational support (OR 1.94; 95% CI 1.01-3.72) and those with more than primary five school level also perceived high levels of informational support (OR 1.94; 95% CI 1.18-3.19) compared to those with less education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".