Sickle cell knowledge, premarital screening and marital decisions among local government workers in Ile-Ife, Nigeria
Bibliographic record
Abstract
Background: In Nigeria, as in the rest of equatorial Africa, sickle cell disease (SCD) has its highest incidence and continues to cause high morbidity and early death. The condition is a major public health problem among the black race. The aim of this survey is to determine the level of knowledge about SCD and the factors associated with its prevention among local government workers in Ile- Ife.Method: This is a cross-sectional descriptive study of the knowledge about SCD, attitude towards premarital sickle cell screening and marital decisions among local government workers in Ile-Ife, Nigeria, using a self-administered questionnaire.Results: 69% of study subjects had poor knowledge of SCD, while attitude towards premarital screening was favourable in 95% of the study subjects. Knowledge and attitude were significantly better among subjects with tertiary education. There was a strong positive association between attitude towards sickle cell screening and a history of undergoing screening or partner screening. Most (86.7%) of the respondents and 74.0% of their partners have had sickle cell screening. One-quarter of married and engaged respondents did not know their partner’s sickle cell status. One-third to two-thirds of study subjects will continue the relationship with their partner when either or both have haemoglobinopathy.Conclusion: This study showed poor knowledge of SCD among the studied subjects. There is a need for more emphasis on health education through programmes promoting sickle cell education. In addition, the development of multifaceted patient and public health education programmes, the intensification of screening for the control of SCD by heterozygote detection, particularly during routine preplacement and premarital medical examinations, and the provision of genetic counselling to all SCD patients and carriers are vital to the identification and care of the couples at risk. These will enhance the capacity of the intending couples to make informed decisions and be aware of the consequences of such decisions. Policies are needed to ensure easily accessible community-wide sickle cell screening and premarital and genetic counselling to achieve the desired decline in new births of children with SCD.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".