Measuring Health Literacy in Primary Healthcare: Adaptation and Validation of a French-Language Version of the Brief Health Literacy Screening among Patients with Chronic Conditions Seen in Primary Care
Bibliographic record
Abstract
BACKGROUND: The Brief Health Literacy Screening (BHLS) is a short self-report instrument developed to identify patients with inadequate health literacy. This study aimed to translate the BHLS into French Canadian (BHLS-FCv) and to evaluate its psychometric properties among patients with chronic conditions in primary care. METHODS: The BHLS was translated into French using the Hawkins and Osborne's method. Content validity was evaluated through cognitive interviews. A validation study of the BHLS-FCv was conducted in two primary care clinics in the province of Quebec (Canada) among adult patients with chronic conditions. Psychometric properties evaluated included: internal consistency (Cronbach's alpha); test-retest reliability (intraclass correlation coefficient); and concurrent validity (Spearman's correlations with the Health Literacy Questionnaire (HLQ)). RESULTS: 178 participants completed the questionnaire at baseline and 47 completed the questionnaire two weeks later over the telephone. The average score was 13.3. Cronbach's alpha for internal consistency was 0.77. The intraclass correlation coefficient for test-retest reliability was 0.69 (95% confidence interval: 0.45-0.83). Concurrent validity with Spearman's correlation coefficient with three subscales of HLQ ranged from 0.28 to 0.58. CONCLUSIONS: The BHLS-FCv demonstrated acceptable psychometric properties and could be used in a population with chronic conditions in primary care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".