Developing and evaluating a relevant and feasible instrument for measuring health literacy of Canadian high school students
Bibliographic record
Abstract
Health literacy has come to play a critical role in health education and promotion, yet it is poorly understood in adolescents and few measurement tools exist. Standardized instruments to measure health literacy in adults assume it to be a derivative of general literacy. This paper reports on the development and the early-stage validation of a health literacy tool for high school students that measured skills to understand and evaluate health information. A systematic process was used to develop, score and validate items. Questionnaire data were collected from 275, primarily 10th grade students in three secondary schools in Vancouver, Canada that reflected variation in demographic profile. Forty-eight percent were male, and 69.1% spoke a language other than English. Bivariate correlations between background variables and the domain and overall health literacy scores were calculated. A regression model was developed using 15 explanatory variables. The R(2) value was 0.567. Key findings were that lower scores were achieved by males, students speaking a second language other than English, those who immigrated to Canada at a later age and those who skipped school more often. Unlike in general literacy where the family factors of mother's education and family affluence both played significant roles, these two factors failed to predict the health literacy of our school-aged sample. The most significant contributions of this work include the creation of an instrument for measuring adolescent health literacy and further emphasizing the distinction between health literacy and general literacy.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".