What are the determinants of variation in caretaker satisfaction with sick child consultations? A cross-sectional analysis in five low-income and middle-income countries
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to explore determinants of variation in overall caretaker satisfaction with curative care for sick children under the age of 5 in five low-income and middle-income countries. DESIGN: A pooled cross-sectional analysis was conducted using data from the Service Provision Assessment. SETTING: We used data collected in five countries (Afghanistan, Democratic Republic of the Congo, Haiti, Malawi and Tanzania) between 2013 and 2018. PARTICIPANTS: Respondents were 13 149 caretakers of children under the age of 5 who consulted for a sick child visit. PRIMARY OUTCOMES MEASURED: The outcome variable was whether the child's caretaker was very satisfied versus more or less satisfied or not satisfied overall. Predictors pertained to child and caretaker characteristics, health system foundations and process of care (eg, care competence, user experience). Two-level logistic regression models were used to assess the extent to which these categories of variables explained variation in satisfaction. The main analyses used pooled data; country-level analyses were also performed. RESULTS: Process of care, including user experience, explained the largest proportion of variance in caretaker satisfaction (13.8%), compared with child and caretaker characteristics (0.9%) and health system foundations (3.8%). The odds of being very satisfied were lower for caretakers who were not given adequate explanation (OR: 0.56, 95% CI 0.46 to 0.67), who had a problem with medication availability (OR: 0.31, 95% CI 0.27 to 0.35) or who encountered a problem with the cost of services (OR: 0.57, 95% CI 0.48 to 0.66). The final model explained only 21.8% of the total variance. Country-level analyses showed differences in variance explained and in associations with predictors. CONCLUSIONS: Better process of care, especially user experience, should be prioritised for its benefit regarding caretaker satisfaction. Unmeasured factors explained the majority of variation in caretaker satisfaction and should be explored in future studies.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".