Explanatory model of South Asian newcomer mother and Canadian-born mother comprehension: A cross-sectional
Bibliographic record
Abstract
Maternal health literacy refers to the skills mothers use to manage their own health and their family’s health in healthcare settings. Newcomer mothers face unique barriers to healthcare access, which can be reduced through health education to improve comprehension. An applied, cross-sectional design was used to recruit 20 English-speaking South Asian newcomer mothers (SANMs) and 20 English-speaking Canadian-born mothers (CANMs). This cross-sectional study utilized fuzzy-trace theory to develop an explanatory model for how mothers comprehend health information, with a focus on gist understanding. A digital survey collected data on the ability to comprehend the main idea of pregnancy health information. Additionally, three validated psychometric instruments were administered to measure differences in functional health literacy. Descriptive statistics were conducted on responses to a questionnaire, and accuracy scores were calculated using observation oriented modelling. Data analysis examined the accuracy of models in explaining patterns of observations, supplemented by a visual "eye test" using a histogram to describe observed events. Results indicate that both samples of mothers self-reported adequate numeracy abilities, but performed poorly on functional assessments. In Experiment 1, CANMs who engaged more frequently in numerical reasoning showed a meaningful, non-random pattern of comprehension regarding the chance of viral infection. In Experiment 2, SANMs who more frequently counted or read numbers demonstrated a meaningful pattern of correctly identifying medication timing. These results suggest that gist-based processing supports comprehension in both samples, but the causal patterns linking numerical engagement and comprehension differ. Numerical reasoning relates to comprehension differently across SANMs and CANMs, and therefore health education must be ethno-culturally responsive. Ultimately, this research highlights the need for ‘kind’ learning environments to help ethno-culturally diverse mothers practice and improve their comprehension in healthcare settings and recommends health numeracy education and medication literacy programs to improve numerical reasoning.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".