Community perceptions of pre-eclampsia and eclampsia in Ogun State, Nigeria: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Pre-eclampsia is a complication of pregnancy responsible for high rates of morbidity and mortality, particularly in sub-Saharan Africa. When undetected or poorly managed, it may progress to eclampsia which further worsens the prognosis. While most studies examining pre-eclampsia have used a bio-medical model, this study recognizes the role of the socio-cultural environment, in order to understand perceptions of pre-eclampsia within the community. METHODS: The study was conducted in Ogun State, Nigeria in 2011-2012. Data were obtained through twenty-eight focus group discussions; seven with pregnant women (N = 80), eight with new mothers (N = 95), three with male decision-makers (N = 35), six with community leaders (N = 68), and three with traditional birth attendants (N = 36). Interviews were also conducted with the heads of the local traditional birth attendants (N = 4) and with community leaders (N = 5). Data were transcribed verbatim and analysed in NVivo 10 software. RESULTS: There was no terminology reportedly used for pre-eclampsia in the native language - Yoruba; however, hypertension has several terms independent of pregnancy status. Generally, 'gìrì âlábôyún' describes seizures specific to pregnancy. The cause of hypertension in pregnancy was thought to be due to depressive thoughts as a result of marital conflict and financial worries, while seizures in pregnancy were perceived to result from prolonged exposure to cold. There seemed to be no traditional treatment for hypertension. However for seizures the use of herbs, concoctions, incisions, and topical application of black soap were widespread. CONCLUSION: This study illustrates that knowledge of pre-eclampsia and eclampsia are limited amongst communities of Ogun State, Nigeria. Findings reveal that pre-eclampsia was perceived as a stress-induced condition, while eclampsia was perceived as a product of prolonged exposure to cold. Thus, heat-related local medicines and herbal concoctions were the treatment options. Perceptions anchored on cultural values and lack of adequate and focused public health awareness is a major constraint to knowledge of the aetiology and treatment of the conditions. A holistic approach is recommended for sensitization at the community level and the need to change the community perceptions of pre-eclampsia remains a challenge. 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".