Developing a Health System Literacy Measure for Chinese Immigrants in Canada: Adapting the HLS19–NAV Scale
Bibliographic record
Abstract
Background: Health system literacy is crucial for immigrants to navigate health care systems and access necessary services. Little is known about how well immigrants understand and use the healthcare system in Canada. This study aimed to adapt and validate a health system literacy scale for the Canadian context (HSL-CAN). Methods: A cross-sectional online survey was conducted from March 11 to July 19, 2024, among Chinese individuals aged 30 or older who have lived in Canada for at least 6 months. The HSL-CAN was developed through a literature review, patient and provider consultation, and adaptation of the European Health Literacy Population Survey 2019–2021 for navigational health literacy measurement (HLS19–NAV) and was then translated into simplified and traditional Chinese. Content validity was evaluated via stakeholders’ feedback, and structural validity was evaluated via exploratory and confirmatory analyses (EFA/CFA). Convergent and discriminant validity, as well as known-group validity, were tested using correlations with the HLS19-SF12, ANOVA (or t-test), and effect size. Internal consistency was measured with Cronbach’s alpha coefficient and composite reliability. Results: Initially, HSL-CAN contained 25 items developed using a five-point Likert response scale. Some minor revisions were made according to the stakeholders’ feedback (n = 12). Five redundancy items were removed based on the EFA. CFA supported a one-factor model with good fit indices (CFI = 0.960, TLI = 0.955, SRMR = 0.033, RMSEA = 0.025), χ2/df = 1.41). The scale showed a solid internal reliability (Cronbach’s alpha = 0.81; composite reliability = 0.812). The HSL-CAN is highly correlated with the “health care” construct but lowly with the “health prevention and promotion” construct of HLS19–SF12. Known-group validity showed large mean differences by education, income, and non-cancer chronic comorbidities and small to moderate mean differences by gender, age groups, employment status, self-rated health, and assistance needed to see a healthcare provider. Conclusions: The HSL-CAN is the first validated instrument to evaluate health system literacy in the Chinese population in Canada. Given strong validity and reliability, the instrument can be useful for research and practice, although further refinement is recommended before using this scale on the general population in Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".