The Maternal Health Literacy of South Asian Newcomer Mothers and Canadian-born Mothers
Bibliographic record
Abstract
Research suggests newcomer mothers score lower than Canadian-born mothers on health literacy (HL) and health numeracy (HN) assessments and have difficulty accessing maternal health services. This dissertation explored the nexus between language, language competencies, and the comprehension of health information by English-speaking, South Asian newcomer mothers (SANMs) and English-speaking, Canadian-born mothers, with a focus on learning. First, we conducted a scoping review using a systematic search strategy to identify conceptualizations of maternal health literacy (MHL) and HN according to the empirical research. Second, we employed narrative inquiry and used thematic analysis in conjunction with propositional analysis to explicate the verbalizations of mothers who shared stories about their comprehension of ultrasound examination preparation, health-risk information, and shared decision making. Third, we determined the accuracy of an explanatory model of qualities of MHL through an observation-oriented investigation of data from an online survey and used a non-parametric procedure to perform comparative analysis of responses. In study one, the final themes of the scoping review of MHL and HN conceptualizations were (i) sociocultural demographics, (ii) self-efficacy, (iii) communication, (iv) information seeking and operationalization, (v) health status, and (vi) reasoning. In study two, the results of the narrative inquiry suggested mothers demonstrate MHL through reifying, posturing, and volition. In study three, the results of the post-hoc analysis of survey data showed SANMs mothers are limited in their functional health literacy (FHL) compared to Canadian-born mothers. The collective contribution of the papers advocates for greater sociocultural linguistic measures of MHL in public health research to account for individual learning behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".