Evaluating the effects of an intervention to improve the health environment for mothers and children in health centres (BECEYA) in Mali: a qualitative study
Bibliographic record
Abstract
Abstract Background An intervention aiming to improve maternal and children environment in healthcare facilities (BECEYA) was launched in three regions of Mali. This study aimed to explore the perceptions and experiences of patients and their companions, community actors and healthcare facilities staff on the effects of the BECEYA intervention in two regions of Mali. Methods We conducted a qualitative study using an empirical phenomenological approach. Through purposive sampling, women who attended antenatal care in the selected healthcare centres, companions, and health facility staff members were recruited. Data were collected during January and February 2020 through semi-structured individual interviews and focus groups. According to Braun & Clarke approach, audio recordings were transcribed verbatim, and a thematic analysis was conducted in five main steps. Results We recruited 26 participants in individual interviews and 20 participants in focus groups. Donabedian conceptual framework of quality of care was used to present the perceived changes following the implementation of the BECEYA project. The themes that emerged from data analysis are perceived changes in terms of infrastructure (perceived changes in the characteristics of the healthcare facilities setting, including the infrastructure introduced by the BECEYA project), process (changes in the delivery and use of care introduced or resulting from BECEYA activities), and outcome (the direct and indirect effects of these changes on the health status of patients and the population). Conclusion The study identified the positive effects of women users of the services, their companions and health centre staff following the implementation of the project. Therefore, this study contributes to the advancement of knowledge to show the link between improving the health environment of health centres in developing countries, a major aspect of the quality of care, and maternal and child health care.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".