Incorporating the patient voice and patient engagement in GOAL‐Hēm: Advancing patient‐centric hemophilia care
Bibliographic record
Abstract
BACKGROUND: Goal Attainment Scaling for Hemophilia (GOAL-Hēm) is a novel, hemophilia-specific, validated patient engagement tool and patient-reported outcome instrument. OBJECTIVE: We evaluated the degree to which the language of GOAL-Hēm was patient-centric and the content valuable and relevant for people with hemophilia (PWH) and/or their caregivers. PATIENTS/METHODS: Patients and caregivers participated in one of three investigations: an online survey, one-on-one patient interviews, or a focus group. The survey and interviews assessed the clarity and relevance of the GOAL-Hēm menu items. Interviews were semistructured, audio recorded, and transcribed verbatim. Feedback from interviews was coded as "clear," "unclear," "remove," or "add." The focus group explored participants' experience of GOAL-Hēm and elicited recommendations for implementation. Quotations from focus group and interview transcripts were indexed and charted to emergent themes for analysis. RESULTS: = 6). After their feedback, 32% (15/48) of goals were retained unchanged. Further feedback resulted in the removal of 45% (286/635) of the goal descriptors, and 30% (193/635) of the retained descriptors were modified. Three new (total = 38) goals and 42 descriptors (total = 368) were added to the menu. Thematic analysis indicated that participants were enthusiastic about patient-centric language, empowered through the goal-setting process, and recognized GOAL-Hēm could measure clinically meaningful change. CONCLUSION: By listening closely to patients and caregivers, we refined GOAL-Hēm to better capture the experiences of PWH, enhance content validity, and augment implementation strategies. Incorporating the patient voice is integral to developing patient-centered outcome measures.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".