Enhancing Engagement to Support Shared Decision Making Within Haemophilia: Development of Materials for People With Haemophilia, Caregivers and Healthcare Professionals
Bibliographic record
Abstract
INTRODUCTION: Treatment decision making can be complex and challenging for people with haemophilia (PwH) and their caregivers. A shared decision-making (SDM) approach can ensure that decisions about treatment and care incorporate patients' beliefs, values, and preferences. AIMS: The aims were to: (1) identify potential drivers and barriers to SDM between PwH/caregivers and clinicians, (2) develop tools to facilitate SDM in clinic appointments, and (3) gain feedback on the usability and design of these tools to inform refinements. METHODS: Virtual focus groups were conducted with adolescent and adult PwH, caregivers of children with haemophilia, and physicians and allied health professionals (AHPs) across Canada to inform the development of tools to support SDM in clinical appointments. The usability and acceptability of the tools were examined in one-to-one interviews. These findings were used to further refine the tools. RESULTS: Thematic analysis from five focus groups (n = 23) identified broad themes pertaining to barriers of SDM, factors influencing treatment decisions, and strategies to facilitate SDM. Insights were used to develop an Appointment Planner including pre-appointment questions and priority setting for PwH/caregivers and a Conversational Prompt Guide including considerations for clinicians. The findings of one-to-one interviews (n = 25) indicated good usability and acceptability and informed minor refinements to the tools. CONCLUSION: The study demonstrates the acceptability of specific tools to facilitate SDM based on qualitative research with the target populations. Initial feedback was positive. Further research is required to assess whether the tools can facilitate SDM by improving communication and strengthening relationships between PwH/caregivers and clinicians.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".