Validation of a French-language version of the health education impact Questionnaire (heiQ) among chronic disease patients seen in primary care: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The Health Education Impact Questionnaire (heiQ) allows for the evaluation of the effects of education interventions provided to patients with chronic diseases. This study describes the process for the cross-cultural adaptation and validation of the heiQ into French (heiQ-Fv). METHODS: We undertook a systematic translation process followed by a validation study based on the secondary analysis of cross-sectional data from a longitudinal study. Participants in the validation study were adult patients from primary care clinics in Quebec, Canada, with one or more of the following diseases: diabetes, asthma, chronic obstructive pulmonary disease, cardiovascular disease; or one or more risk factors for these diseases. Main outcomes of the study were the French version of the heiQ-Fv and the validation analyses that included internal consistency, test-retest reliability, confirmatory factor analysis (CFA) and concomitant validity. RESULTS: The validation analysis was conducted on results from 332 participants. Cronbach's alphas (internal consistency) for seven domains of the heiQ-Fv varied from 0.80 to 0.89; one domain scored 0.69. The test-retest analysis (n = 50) yielded intra-class correlation coefficients from 0.66 to 0.86. The CFA of the eight heiQ domains with the hypothesis of no correlation between the domains yielded a model that did not exhibit acceptable fit values. A model with the hypothesis of all domains correlated exhibited acceptable fit values (scaled chi-square = 1210.15, degrees of freedom = 712, p < 0.001; CFI = 0.98; RMSEA = 0.06; SRMR = 0.065). Results show a moderate correlation (concomitant validity) between five domains of the heiQ-Fv and the Self-Efficacy for Managing Chronic Diseases. We also found a moderate to strong correlation between the Emotional Wellbeing domain of the heiQ and the Kessler Psychological Distress Scale (K6) (r = 0.61; 95 % CI: 0.52 -0.69, p < 0.01). CONCLUSIONS: The heiQ was translated into French using a rigorous translation process; the French-language version showed good psychometric properties. Health professionals and researchers in primary care settings may use the heiQ-FV to evaluate the impact of educational programs on patients with chronic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".