Perceived confidence for injury self‑management increases for young men with mild haemophilia with the use of the mobile app HIRT?
Bibliographic record
Abstract
Abstract Background: Young men with mild haemophilia have unique challenges pertaining to bleed management. They may not always identify musculoskeletal injury requiring medical attention as they do not bleed frequently, potentially resulting in significant health consequences. In response to these challenges, a team of clinicians, researchers and young men with mild haemophilia developed a self-assessment pathway which was converted into a mobile app. Aim: This study examined the influence of the mobile app, HIRT? (Hemophilia Injury Recognition Tool) on perceived injury self-management in young men with mild haemophilia in Canada. Methods: We used a mixed methods design. The quantitative data, through a self-report questionnaire, evaluated perceived injury self-management strategies and participant confidence levels. Non-parametric Wilcoxon signed-rank test and McNemar chi-square test were used to determine association between perceived self-management strategies when using and not using the app, with significant levels set at p<0.05. Qualitative data was created using interpretive description and inductive content analysis of recorded and transcribed interviews. Results: 12 young men, aged 18-35 years, participated. Perceived confidence levels significantly increased (p=0.004) with the use of the app. Five qualitative themes were identified: accessibility, credibility, the benefit of alarms, confidence and usefulness. Conclusion: This study provides promising evidence to support the feasibility and use of HIRT? as an injury self-management tool for young men with mild haemophilia. Future research should prospectively investigate the effect of the app on injury selfmanagement confidence.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".