Qualitative Evaluation of a Novel Educational Tool to Communicate Individualized Hip Fracture Prognostic Information to Patients and Surrogates: My Hip Fracture (My-HF)
Bibliographic record
Abstract
INTRODUCTION: (My-HF), to provide patients and SDMs of patients hospitalized with acute HF individualized estimates of their post-HF prognosis. We conducted initial usability testing of My-HF in a sample of patients with HF and SDMs. MATERIALS AND METHODS: My-HF provides information about: 1) anatomy and risk factors for HF; 2) Hip fracture treatment received; 3) individualized predicted risk of adverse events and 4) anticipated discharge trajectory. We conducted a qualitative usability study using a convenience sample of hospitalized, post-operative patients with acute HF or SDMs of patients who lacked decision-making capacity. We used semi-structured interviews to obtain feedback. Thematic analysis was used to identify themes and concepts. RESULTS: We conducted interviews with 8 patients and 9 SDMs (mean age of interviewees 70.1 years, 41% female). My-HF was generally well received. Thematic analysis identified legibility and visual appeal, comprehension, numeracy, utility and reflection as prominent themes. Most respondents found My-HF to be useful in improving their understanding of HF and as a potential mechanism for sharing information with other care team members (including family and professionals). Suggestions for improvement of legibility, presentation of the individualized prognosis information and content were identified. DISCUSSION: Patients and SDMs are generally accepting of My-HF and found it useful for communicating individualized prognostic information. Feedback identified areas for improvement for future iterations of the tool. CONCLUSION: My-HF presents a means of addressing the gap in understanding of prognosis post-HF as a part of patient-centered care. Further evaluation will be needed to assess the impact of My-HF on patient and SDM reported outcomes as we transition from a paper to smart-phone enabled web application.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".