Pilot Quality Improvement Study of SafeBack
Bibliographic record
Abstract
Caregivers, including nurses, personal support workers, and family caregivers, have a high risk of low back pain due to the patient-handling postures they use. Often, caregivers place the needs of the care recipient above their own. This means that caregivers may use postures that put them at risk of injury. Often times, these caregivers may not even be aware of the potential for injury due to their posture selection. Therefore, there is a need for easy-to-use, inexpensive, and accessible education and training tools that can be used to guide the use of safer patient-handling postures. To address this need, we have developed SafeBack: a mobile application for estimating low back forces in either real-time or from photographs taken of the postures in need of evaluation. The app requires user- posturing of skeleton, and the input of anthropometric and load data before low back compression forces can be estimated. The purpose of this study was to run a pilot quality improvement study to gather user feedback about the SafeBack App. The results show that the App, in its current form, has acceptable usability, but that improvements are needed before it can be used as a post ure training and/or injury prevention tool.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".