Empowering mothers: Advancing maternal health literacy and numeracy through the introduction of Maternal and Child Health Calendar
Bibliographic record
Abstract
BACKGROUND: The health literacy and numeracy skills of women in Pakistan are very low compared to other low- and middle-income countries. OBJECTIVE: The aim of this study was to improve the health literacy and numeracy skills of unschooled women in Northern Pakistan by developing a Maternal and Child Health Calendar (MCHC). The MCHC utilizes locally contextualized icons to promote and enhance service utilization and maternal and child health (MCH) outcomes. METHODS: We conducted a qualitative exploratory study design to understand the experiences and usefulness of the MCHC among women. We recruited the participants using purposive sampling. Using a semi-structured interview guide, we conducted individual interviews with nine Key informants, that is, Agha Khan Rural Support Staff and Community-based savings group staff and five focus group discussions with unschooled women. We followed Braun and Clarke's steps to conduct an inductive thematic data analysis. RESULTS: The findings of our study are categorized into the following themes: (1) the benefits of using MCHC, (2) the usefulness of the MCHC in women's healthcare decision-making, (3) empowerment of poorly schooled women, (4) enabling numeracy and record-keeping skills, (5) MCHC implementation challenges, and (6) participants suggestions to improve the MCHC. Our findings revealed that the MCHC improved the health literacy and numeracy of illiterate or less educated women by using localized images to help them comprehend their own and their children's health. Additionally, it effectively empowered these women in their healthcare decision-making, such as discussing family planning with their husbands. Women also suggested modifying some images in the MCHC to enhance their clarity and usefulness. CONCLUSION: The MCHC has the potential to safely and sustainably build basic MCH literacy and numeracy skills among both literate and illiterate women in Northern Pakistan. Further research is needed to assess its potential as a stand-alone intervention to improve MCH outcomes.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".