Measurement and analysis of inequality of opportunity in access of maternal and child health care in Togo
Bibliographic record
Abstract
BACKGROUND: Access to maternal and child health care in low- and middle-income countries such as Togo is characterized by significant inequalities. Most studies in the Togolese context have examined the total inequality of health and the determinants of individuals' health. Few empirical studies in Togo have focused on inequalities of opportunity in maternal and child health. To fill this gap, we estimated changes in inequality of opportunity in access to maternal and child health services between 1998 and 2013 using data from Togo Demographic and Health Surveys (DHS). METHOD: We computed the Human Opportunity Index (HOI)-a measure of how individual, household, and geographic characteristics like sex and place of residence can affect individuals' access to services or goods that should be universal-using five indicators of access to healthcare and one composite indicator of access to adequate care for children. The five indicators of access were: birth in a public or private health facility; whether the child had received any vaccinations; access to prenatal care; prenatal care given by qualified staff; and having at least four antenatal visits. We then examined differences across the two years. RESULTS: Between 1998 and 2013, inequality of opportunities decreased for four out of six indicators. However, inequalities increased in access to antenatal care provided by qualified staff (5.9% to 12.5%) and access to adequate care (27.7% to 28.6%). CONCLUSIONS: Although inequality of opportunities reduced between 1998 and 2013 for some of the key maternal and child health indicators, the average coverage and access rates underscore the need for sustained efforts to ensure equitable access to primary health care for mothers and children.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".