“I had to change my attitude”: narratives of most significant change explore the experience of universal home visits to pregnant women and their spouses in Bauchi State, Nigeria
Bibliographic record
Abstract
BACKGROUND: Universal home visits to pregnant women and their spouses in Bauchi State, northern Nigeria, discussed local evidence about maternal and child health risks actionable by households. The expected results chain for improved health behaviours resulting from the visits was based on the CASCADA model, which includes Conscious knowledge, Attitudes, Subjective norms, intention to Change, Agency to change, Discussion of options, and Action to change. Previous quantitative analysis confirmed the impact of the visits on maternal and child outcomes. To explore the mechanisms of the quantitative improvements, we analysed participants' narratives of changes in their lives they attributed to the visits. METHODS: Local researchers collected stories of change from 23 women and 21 men in households who had received home visits, from eight male and eight female home visitors, and from four government officers attached to the home visits program. We used a deductive thematic analysis based on the CASCADA results chain to analyze stories from women and men in households, and an inductive thematic approach to analyze stories from home visitors and government officials. RESULTS: The stories from the visited women and men illustrated all steps in the CASCADA results chain. Almost all stories described increases in knowledge. Stories also described marked changes in attitudes and positive deviations from harmful subjective norms. Most stories recounted a change in behaviour attributed to the home visits, and many went on to mention a beneficial outcome of the behaviour change. Men, as well as women, described significant changes. The home visitors' stories described increases in knowledge, increased self-confidence and status in the community, and, among women, financial empowerment. CONCLUSIONS: The narratives of change gave insights into likely mechanisms of impact of the home visits, at least in the Bauchi setting. The compatibility of our findings with the CASCADA results chain supports the use of this model in designing and analysing similar interventions in other settings. The indication that the home visits changed male engagement has broader relevance and contributes to the ongoing debate about how to increase male involvement in reproductive health.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".