Use of standardized outcome measures for people with lower-limb amputation: A survey of prosthetic practitioners in Canada
Bibliographic record
Abstract
BACKGROUND: Outcome measures (OMs) are useful tools; however, clinicians may find implementing them into clinical practice challenging. OBJECTIVES: To characterize Canadian prosthetists' use of OMs for people with lower-limb amputation, including motivations for use, comfort selecting OMs, resources available for administration, and barriers to implementation. METHODS: A cross-sectional study was conducted between March and July 2021. Orthotics Prosthetics Canada sent Canadian prosthetists an invitation to take the online survey. RESULTS: Forty-nine Certified Prosthetists completed the survey. Only 16% of participants reported that they were expected to use OMs. Participants reported being more comfortable administering performance-based OMs than self-report surveys. More than two-thirds of participants agreed that OMs "can be administered with knowledge they have" and are "within their scope of practice." However, less than 25% agreed that OMs are "administered in a standardized way in the profession," and less than 40% indicated they are "easy to make part of my routine." Participants reported they generally have time and space to do OMs, but there was no agreed-on reason to use them. CONCLUSIONS: Use of OMs among Canadian prosthetists seems to be low relative to prosthetists in the United States. Education, financial incentives, or changes to professional expectations are likely needed to increase routine OM use. Efforts to improve the standardization of OM administration and ease the incorporation of OMs into routine practice may also increase use. Canadian prosthetists may elevate their standards of clinical practice and better understand the impact of prosthetic treatments on their patients by more routinely using OMs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".