Évaluation de la qualité des services de santé maternelle et néonatale en Guinée-Conakry et au Togo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it