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 Canada context (HSL-CAN) using factor analysis approach. Methods: A cross-sectional online survey was conducted from March 11st to July 19th, 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). The measure was translated into Simplified and Traditional Chinese, and its content was evaluated by stakeholders’ feedback. Structural validity was evaluated using exploratory (EFA) and confirmatory (CFA) factor analysis. Convergent and discriminant validity were tested using correlations with the HLS19-SF12 and known-group validity was evaluated by using ANOVA or ttest and reporting effect size. Internal consistency was evaluated through Cronbach’s alpha coefficient and composite reliability. Results: Initially, HSL-CAN contained 25 items developed using a 5-Likert response scale. Some minor revisions were made according to the stakeholders’ feedback (n=12). Five items with factor loading < 0.4 were removed based on the EFA. The one-factor CFA satisfied good fit indices: CFI=0.960, TLI=0.955, SRMR=0.033, RMSEA (90%CI) = 0.025(0.016-0.032), and χ^2/df ratio of 1.41. The scale showed a solid internal reliability (Cronbach’s alpha=0.81; composite reliability=0.812). For convergent and discriminant validity, the HSL-CAN showed a high correlation with the ‘health care’ construct but a low correlation with the ‘health prevention and promotion construct’ construct of HLS19 – SF12. Known-group validity showed large mean differences by education, income, 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. Conclusion: Study findings provide evidence that the HSL-CAN is a valid and reliable tool for evaluating health system literacy in Chinese population in Canada. However, further refinement is recommended before using this scale to 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".