Community health workers’ knowledge and practice in relation to pre-eclampsia in Ogun State, Nigeria: an essential bridge to maternal survival
Bibliographic record
Abstract
BACKGROUND: Pre-eclampsia is a leading cause of maternal and fetal morbidity and mortality worldwide. Early detection and treatment have been instrumental in reducing case fatality in high-income countries. To achieve this in a low-income country, like Nigeria, community health workers who man primary health centres must have adequate knowledge and skills to identify and provide emergency care for women with pre-eclampsia. This study aimed to determine community health workers' knowledge and practice in the identification and treatment of pre-eclampsia, as they are essential providers of maternal care services in Nigeria. METHODS: This study was part of a multi-country evaluation of community treatment of pre-eclampsia. Qualitative data were obtained from four Local Government Areas of Ogun State, in south western Nigeria by focus group discussions (N = 15) and in-depth interviews (N = 19). Participants included a variety of community-based health care providers - traditional birth attendants, community health extension workers, nurses and midwives, chief nursing officers, medical officers - and health administrators. Data were transcribed and validated with field notes and analysed with NVivo 10.0. RESULTS: Community-based health care providers proved to be aware that pre-eclampsia was due to the development of hypertension and proteinuria in pregnant women. They had a good understanding of the features of the condition and were capable of identifying women at risk, initiating care, and referring women with this condition. However, some were not comfortable managing the condition because of the limitation in their 'Standing Order'; these guidelines do not explicitly authorize community health extension workers to treat pre-eclampsia in the community. CONCLUSION: Community-based health care providers were capable of identifying and initiating appropriate care for women with pre-eclampsia. These competencies combined with training and equipment availability could improve maternal health in the rural areas. There is a need for regular training and retraining to enable successful task-sharing with these cadres. TRIAL REGISTRATION: NCT01911494 .
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".