Factor Structure and Equivalence of Maternal Resources for Care in Bangladesh, Vietnam, and Ethiopia
Bibliographic record
Abstract
OBJECTIVES: Resources for care among women are crucial for children's growth and development. The objectives of this cross-sectional study were to determine if: (1) the factor structure of measures of maternal resources for care was comparable across countries and consistent with the theoretical constructs and (2) the measures showed equivalence across contexts. METHODS: The study included 4400, 4029 and 2746 women from Bangladesh, Vietnam, and Ethiopia, respectively. The measures of resources for care were maternal education, knowledge, height, body mass index, mental well-being, financial autonomy, decision-making, employment, support in chores, and perceived support. RESULTS: The factor analysis demonstrated that a two-factor solution best explained the structure of resources for care in all three countries. The first factor was associated with financial autonomy and employment in all three countries and with decision-making in two countries. The second factor was associated with education and knowledge in all three countries. The measures of resources for care had measurement equivalence across countries. CONCLUSION FOR PRACTICE: Resources for care were structurally similar and measurement equivalent across countries and can be used for measurement in low- and middle-income countries. Additional work examining the structure and cross-context equivalence of resources for care in other settings is warranted.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".