The perspective of rehabilitation health care professionals regarding the clinical utility of a body-environment proximity measurement device
Bibliographic record
Abstract
Measuring the proximity of the body to the environment has the potential to provide rehabilitation health care professionals (RHCP) new, additional information to assess mobility. The aims of this study were to explore the opinions and needs of professionals to use a new device to measure such proximity, and to determine potential barriers and facilitators to implement it within the clinical setting. Four focus groups were conducted with 23 RHCP. The concept of a proximity measurement device in the early stages of development was introduced. A qualitative content analysis using a model of clinical utility as guidance for coding of the transcribed texts was used. Many potential uses were raised including: improving patient safety, motor learning, independence in tasks as gait, transfers and reaching tasks, saving time during assessment, improving follow-up with the client, data precision and objectivity to assess tasks. Barriers were related to still unclear relevance, effectiveness, the added value of such a device, and time required for use. The development of a turnkey device with support to facilitate its implementation was seen to be positive. These focus groups provided important information to further identify potential uses of such a device and to help to decrease barriers to its implementation.
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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 | 0.040 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".