Improving Women’s Happiness and Self-Rated Health and Social Capital in Rural Cambodia
Bibliographic record
Abstract
Achieving a state of happiness and good health is undoubtedly important for people’s mental health. It is a challenging feat in any country, but more so in developing countries. However, scant research exists on the happiness and self-rated health of people in developing countries. To examine the impact of social capital on women’s happiness and self-rated health in rural Cambodia, this study applies ordinal logit regression on the interview data of 283 women living in Siem Reap. The following factors positively impacted the happiness of the women: high household income, lending of money to others, high level of trust toward family, social participation, and having high self-rated health. The following factors were associated with higher self-rated health, similarly as with happiness: high level of trust toward family, lending of money to others, social participation, and higher number of surviving children. However, self-rated health decreased when the women were of advanced age, had given birth to many children, or had received poverty identification. The novelty of this study is that it provides a valuable insight into the impact of social capital on the happiness and self-rated health of women in rural Cambodian villages.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".