Numeracy and Health Numeracy Among Chinese and Kenyan Immigrants to Canada
Bibliographic record
Abstract
Mathematics self-efficacy plays an important role in the performance of quantitative tasks. The objective of this study was to examine the role of mathematics self-efficacy in numeracy and health numeracy among immigrants (60 Mandarin-speaking and 60 Kikuyu-speaking immigrants to Canada) for whom English was a second language (ESL). Two French Kit numeracy tasks (the addition, and the addition and subtraction correction tasks) constituted objective measures of numeracy, and the numeracy component of a health literacy instrument (The Short Test of Functional Health Literacy for Adults [S-TOFHLA]) constituted the health numeracy measure. We measured math self-efficacy using the Mathematics Self-Efficacy Scale (MSES) and the Subjective Numeracy Scale (SNS). All measures were presented in English. Kikuyu speakers had lower math self-efficacy ( p < .05) compared with Mandarin speakers ( p < .05). Mandarin speakers outperformed Kikuyu speakers in numeracy ( p < .001) and health numeracy ( p < .023) skills. In multiple regression analyses, 32% to 57 % of the variance in objective numeracy was explained by language, residency in Canada, and self-efficacy, adjusting for the other predictor variables; self-efficacy and format of numeric health information (numbers only vs. numbers with detailed text) consistently predicted health numeracy. Mathematics self-efficacy may be an important factor in numeracy, but not as important in health numeracy for Kikuyu- and Mandarin-speaking immigrants for whom English is a second language. Factors contributing to low numeracy may differ from those explaining low health numeracy in ESL immigrants.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".