Validity and Reliability of Persian Version of Henry Ford Hospital Headache Disability Inventory Questionnaire
Bibliographic record
Abstract
BACKGROUND: A limited number of headache disability indices exist that can evaluate and manage different disabilities related to headache among Iranian patients. OBJECTIVE: This study aimed to translate and validate the Persian version of the Henry Ford headache disability inventory (HDI). METHODS: The original questionnaire was translated and culturally adapted to the Persian setting. A total of 250 patients with chronic headache were enrolled in this study. The questionnaire's face validity, content validity, and convergent validity with Short-Form Health Survey (SF-36) were evaluated and a confirmatory factor analysis (CFA) was conducted. Its internal consistency was also assessed and its short- and long-term test-retest reliability were examined by intraclass correlation coefficient (ICC). RESULTS: The content validity indices were 0.85, 0.99, and 0.97 for simplicity, relevance, and clarity, respectively. The content validity ratio was calculated as one for all items. The findings of CFA confirmed that this index had a good fit. Cronbach's alpha was 0.91, 0.82, and 0.86 for the entire questionnaire as well as its functional and emotional subscales, respectively. The ICC was also calculated as 0.97 for the total inventory. The convergent validity showed significant negative correlations between HDI and short-form health survey items. CONCLUSION: The validity and reliability of the Persian version of the HDI were confirmed. This questionnaire can explore the disabilities of Persian-speaking people with headache disorders.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".