Using an educational intervention to assess and improve disease‐specific knowledge and health literacy and numeracy in adolescents and young adults with haemophilia A and B
Bibliographic record
Abstract
INTRODUCTION: Health literacy (HL) and health numeracy (HN) are underestimated barriers to treatment adherence in patients with haemophilia. AIM: To test the ability of an educational intervention to improve knowledge, HL, HN, adherence and joint health in adolescent and young adult (AYA) males with haemophilia. METHODS: We performed a longitudinal pilot study of 41 participants aged 12-21 years with haemophilia A or B during two clinic visits 6-12 months apart. The first visit included a comprehensive pre-intervention assessment: demographics, knowledge survey, Montreal Cognitive Assessment testing, 5-question tool to assess baseline HN, assessment of HL with the Rapid Estimate of Adolescent Literacy in Medicine tool, history of adherence and Haemophilia Joint Health Score (HJHS). An educational intervention using a visual aid explained basic pharmacokinetic (PK) concepts and personal teaching regarding haemophilia treatment regimens was used during this visit. The second visit included a post-intervention assessment: a reassessment of knowledge, HL, HN, HJHS, adherence to prescribed therapy and number of joint bleeds since the pre-intervention visit. RESULTS: Forty-one males with haemophilia A or B were enrolled in the study. Of these, 33 completed the post-intervention assessment. Knowledge (p = .002) and HN (p = .05) were significantly improved post-intervention, although the HL, number of joint bleeds, adherence to prescribed therapy and HJHS were not. CONCLUSIONS: Participants with low HL and/or HN may benefit from alternate methods of education such as audiovisual material. Education using audiovisual materials improved knowledge and HN in this study; however, this did not affect adherence to prescribed therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".