Spousal Support during Pregnancy in the Nigerian Rural Context
Bibliographic record
Abstract
Abstract BackgroundPregnancy constitutes a global health concern, thus the need for spousal support during this period cannot be overemphasised. This study examined the kinds of support pregnant women expected and received from their spouse as well as the effect of such supports during pregnancy, labour and delivery. MethodsThe study adopted both quantitative and qualitative methods of data collection. The respondents were selected using multistage and simple random sampling techniques. ResultsFindings showed that respondents expected and received maximum support from their spouses during pregnancy, labour and delivery. Spiritual support such as praying and fasting were top on the kinds of support pregnant women expected and received from their husbands during pregnancy and delivery. Others include, helping in house chores, financial provision, taking care of other children, accompanying to labour room, and sexual support. More than three-quarter of the respondents stated that maximum support from their husbands made pregnancy, labour and delivery easier. Cramer’s V showed that the association between support and husbands’ occupation was 0.233 and Pearson Chi-square showed that the association was statistically significant χ2(2) = 27.894,p< .001. ConclusionThe study concluded that spousal support during pregnancy was high among rural women in South-western Nigeria, and it impacted positively on their wife’s period of pregnancy, labour and delivery. The high level of spousal support should be sustained so as to promote family bonding and development.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".