Measuring the impact of hemophilia on families: Development of the Hemophilia Family Impact Tool (H‐FIT)
Bibliographic record
Abstract
Introduction This study aimed to assess the impact of hemophilia on families, in the context of current and emerging hemostatic therapies, and explore the need for a hemophilia-specific tool targeted at parents of boys aged <4 years. A secondary aim was to develop and validate the new tool. Methods Focus groups were conducted with parents of boys with hemophilia and hemophilia health care providers at Canadian hemophilia treatment centers (HTCs) to review the relevance of the Pediatric Quality of Life Family Impact Module (PedsQL-FIM); a novel questionnaire was developed by identifying core themes expressed. This questionnaire, the Hemophilia Family Impact Tool (H-FIT) was validated in a sample of parents of boys with hemophilia relative to the PedsQL-FIM. Results Seven focus groups were conducted at four HTCs, generating themes specific to hemophilia not covered by the PedsQL-FIM, suggesting that a new tool be developed (the H-FIT). In the validation phase, 54 parents completed the H-FIT and PedsQL-FIM. The H-FIT had a strong correlation with the PedsQL-FIM across all ages (r = 0.79; P < .0001) and a moderate correlation for parents of boys aged <7 years (r = 0.64; P = .0007). There was a significant difference between the mean H-FIT scores for parents of boys using extended half-life factor (68.1; standard deviation [SD]=14.2) compared to standard half-life factor (54.7; SD=18.4; P = .04). Conclusion A novel, disease-specific tool, the H-FIT, has been developed to measure the impact of hemophilia on families. The H-FIT has good preliminary measurement properties and may be responsive to changes in therapy associated with a decreased burden of administration.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".