Effect of Social Media Spiritual and Emotional Abuse Messages on Comprehension and Behavioural Practices of Married Couples in Lagos State, Nigeria
Bibliographic record
Abstract
Evidence from media reports affirmed a progressive increase in incidences of domestic violence globally and in Nigeria. Spiritual and emotional abuse which may have negative consequences on the emotional and psychological wellbeing of the victims are existing forms of domestic violence that are taken with levity based on the entrenched religious and patriarchal systems that are perceived as customary in the Nigerian society. The major objective of this study was to to determine the effect of social media spiritual and emotional abuse messages on comprehension and behavioral practices of married couples in Lagos State, Nigeria. The study employed the true-experimental design for randomization of participants into the experimental and control groups. Study population was non-finite and participants were selected using the proportionate, convenience and purposive sampling techniques. Out of the selected 180 couples, 176 met the inclusion criteria set for the research. Using the Self-structured questionnaire, the results showed a significant difference in married couples’ knowledge, comprehension and behavioural practices of spiritual and emotional abuse, before and after the social media intervention. The study concluded that lack of knowledge and comprehension regarding spiritual and emotional abuse is based on dearth of information. This is an indication of intervention effectiveness. Increased inspiring campaigns and programs on social media that will involve married people and the general public in generating contents on spiritual and emotional abuse was recommended. This would induce intentional sourcing for domestic violence information on social media.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".