Improving shared decision-making for prosthetic care: A qualitative needs assessment of prosthetists and new lower-limb prosthesis users
Bibliographic record
Abstract
BACKGROUND: Prosthesis design is complex and multiple appropriate options exist for any individual with lower-limb amputation. However, there is insufficient evidence for guiding decision-making. Shared decision-making (SDM) offers an opportunity to incorporate patient-specific values and preferences where evidence is lacking for prosthesis design decisions. To develop resources to facilitate SDM, and consistent with the International Patient Decision Aid Standards, it is necessary to identify the decisional needs of prosthetists and prosthesis users for prosthesis design decisions. OBJECTIVES: To assess the needs of prosthetists and new prosthesis users for SDM about the first prosthesis design. STUDY DESIGN: Qualitative descriptive design. METHODS: Six focus groups were conducted with 38 prosthetists. Individual semistructured interviews were conducted with 17 new prosthesis users. Transcripts were analyzed using directed content analysis, with codes defined a priori using existing frameworks for SDM: the Three Talk Model for SDM and the Ottawa Decision Support Framework. RESULTS: Four main themes were identified among prosthetists and prosthesis users: acknowledging complexity in communication, clarifying values, recognizing the role of experience to inform preferences , and understanding the prosthetic journey . CONCLUSIONS: Resources that support SDM for the first prosthesis design should consider methods for identifying individual communication needs, support with clarifying values, and resources such as experience for achieving informed preferences, within the context of the overall course of rehabilitation and recovery following lower-limb amputation. The themes identified in this work can inform SDM to promote collaborative discussion between prosthetists and new prosthesis users when making prosthesis design decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".