Évaluation de la qualité des services de santé maternelle et néonatale en Guinée-Conakry et au Togo
Bibliographic record
Abstract
INTRODUCTION: The quality of mother and child healthcare remains a challenge for low and middle income countries. Quality interventions that allow a reduction of maternal and infantile mortality require the services of qualified personnel. The objective of this study is to present the results of analysis of the status of human healthcare resources and the quality of healthcare (technical, interpersonal, organisational) they provide to mothers and neonates in Guinea and Togo. METHODS: Data were derived from Guinea and Togo case studies with embedded levels of analysis. Participants were: maternal and neonatal health care resources (MNCHR), health care beneficiaries, community members. Data collection methods comprised: observations of MNHCR clinical practice; interviews (beneficiaries, health care establishment and educational institution personnel); and focus groups (men, women, community leaders, students). Analysis consisted of qualitative analysis of the content of interviews and focus groups and quantitative analysis of quality scores. RESULTS: The observations revealed a low level of health care quality for all criteria. Non-technical quality varied according to: the health establishment and level of experience, the MNHCR qualifications, specialisation and basic training. Geographic and financial accessibility, maternal and neonatal health care personnel capacities, continuity and extent of their services are unsatisfactory. CONCLUSION: Recommendations target the establishment of public policies to reinforce MNHCR capacities, standard to define their practice, and organisation and work environment. Conclusions could be used as benchmarks for other countries from Sub-Saharan Africa.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".