The Maternal Health Literacy of South Asian Newcomer Mothers and Canadian-born Mothers: A Narrative Inquiry Using Propositional Analysis
Bibliographic record
Abstract
Objective: First, this comparative analysis of comprehension aimed to guide care and assist with health education research and practice as indicated in mothers stories of learning. Second, this research aimed to determine the comprehension processes of participants by comparing and characterizing the quantity and quality of the concepts’ mothers use in their narratives. Maternal health literacy (MHL) allows mothers to apply health information across all healthcare settings to make decisions about their health. Comprehension and reasoning are essential MHL skills for applying health advice. Methods: Using narrative inquiry methodology and transformative learning theory as the lens, the comprehension processes of English-speaking South Asian Newcomer Mothers (SANMs) (n=7) were compared with those of English-speaking Canadian-born mothers (n=7). Through semi-structured interviews, the mothers discussed their comprehension of ultrasound examination preparation, health risk information, and shared decision making. Themes were identified using inductive thematic analysis, with two reviewers identifying latent themes concerning situational and sociocultural MHL practices. Then, excerpts were explicated using propositional analysis, which systematically identified the features of thought and behavior at an individual level, to identify semantic features of discourse comprehension as a form of methodological triangulation. Next, the narratives were quantified to identify latent patterns of comprehension processes, conceptual knowledge, and semantic discourse features. Results: The key findings indicated that mothers demonstrate MHL through reifying, posturing, and volition, and that all mothers engage in knowledge-building activities and experiential learning in their communities to learn relationally from each other. Conclusion: Ultimately, MHL empowers all mothers to become more engaged during medical appointments and in health decision making.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".