Prevalence of Spousal Abuse among Married Persons in South East Nigeria: Implications for Counselling
Bibliographic record
Abstract
The study investigated prevalence of Spousal Abuse among Married Persons in South-East Nigeria: Implications for Counselling. Three research questions were formulated to guide the study. The research design adopted for the study was Ex-post Facto. The instrument used to collect data for the research was the “Prevalence of Spousal Abuse Among Married Persons Questionnaire” (POSAAMPQ). Content and facial validity of the instrument were established through expert judgement. The instrument has a reliability coefficient of 0.85 using test re-test method. The investigator used 3 research Assistants to administer 230 copies of the questionnaire on 230 respondents married persons in the 3 states where the investigation was conducted. The data collected from the field work were collated and standard deviation and mean score analysis were carried out for the 15 items in order to answer the 3 research questions raised in the study. The benchmark of 2.50 was chosen for either agreeing or disagreeing with each of the 15 items. The study revealed that there was prevalence of economic abuse among the married persons, there was no prevalence of physical abuse among the married persons. Finally, the investigation indicated that there was prevalence of verbal abuse among the married persons. Some of the recommendations include: Spouses should develop mutual respect for themselves and thus refrain from taking their partners’ money without permission, couples should have a thorough understanding of their partners so as to avoid issues that may trigger off anger and thereby prevent wanton destruction of properties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".