KNOWLEDGE AND ATTITUDE OF PREGNANT WOMEN ON THE CAUSES AND PREVENTION OF PRE-ECLAMPSIA AT OLABISI ONABANJO UNIVERSITY TEACHING HOSPITAL, SAGAMU, OGUN STATE, NIGERIA
Bibliographic record
Abstract
Abstract Pre-eclampsia is one of the common complications of pregnancy and continues to be a leading cause of death and disability globally. Despite the effort of the government and other developmental agencies to reduce maternal death rates globally. Therefore, this study assessed the knowledge and attitude of pregnant women attending antenatal clinic on the causes and prevention of pre-eclampsia in Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State. The study utilized a descriptive survey. Participants that were involved in the study were 242 pregnant women attending antenatal clinic using convenience sampling techniques. Data was collected using a structured questionnaire. Data obtained from the respondents were analyzed using Statistical Packages for Social Sciences (SPSS v.21). The result revealed that majority of the respondents (50.5%) were between the ages of 25-29 years, more than half of the respondents (57.5%) had high level of knowledge towards pre-eclampsia while few (42.5%) had low level of knowledge towards pre-eclampsia. Less than half of the respondents (41.5%) obtained knowledge from the hospital and 4.5% from newspaper. This showed that majority of the respondents knew about pre-eclampsia. Majority of the respondents believed that any symptom felt during pregnancy should be reported; headache (58%), swollen legs (62%), lower or upper abdominal pain (75%) and rapid weight gain (66%) and this revealed a positive attitude towards pre-eclampsia. Majority (75%) of the pregnant women believed that there is a known cause of pre-eclampsia. The study revealed that majority (80%) believed that pre-eclampsia is preventable, through regular antenatal clinic (92.5%), avoiding stress (90.5%), avoiding excess salt intake (84.5%) and having adequate rest (88%), while few (20%) believed pre-eclampsia is not preventable. In conclusion, majority of the respondents had good knowledge and attitude towards the prevention of pre-eclampsia. Awareness should be created among antenatal mothers on their obstetric health status through antenatal health talk, newspaper, radio, social media, conferences, workshop by the community, local government, state government and federal government.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".