Safe Patient Handling Musculoskeletal Injury-Prevention Smartphone App for Community Health Care Workers: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Safe patient handling is critical for reducing musculoskeletal injuries among health care workers; yet, community health care workers often face barriers such as limited access to training and real-time resources. OBJECTIVE: This study had three objectives: (1) provide detailed insights into the unmet needs of Island Health community health care workers with respect to safe patient handling resources and access to information, (2) translate those needs into a user-centered prototype of the Safe Patient Handling Musculoskeletal Injury-Prevention (SPH MSIP) smartphone app through an iterative co-design process, and (3) establish the acceptability and feasibility of SPH MSIP app to support community health care workers' safe patient handling practices using a mixed methods design. METHODS: A 3-phase participatory study was conducted. Phase 1 identified unmet safe patient handling needs through participatory meetings with 6 community health care workers, aligning with objective 1. Phase 2 involved developing the SPH MSIP app using co-design methods, integrating user feedback to address challenges such as guidance for high-risk tasks and intuitive design, addressing objective 2. Phase 3 evaluated the app's feasibility and acceptability, aligning with objective 3. The study recruited 28 participants who used the app for one month. A single-group mixed methods design was used, incorporating quantitative metrics such as recruitment (≥50%), retention (≥75%), and satisfaction (mean score ≥4). Qualitative feedback was gathered through small-group interviews to understand usability, usefulness, and integration into workflows. RESULTS: In phase 1, community health care workers identified barriers, including limited safe patient handling, refresher training, and isolation during tasks. In phase 2, the app was developed to address these safe patient handling needs, incorporating features like scenario-specific guidance for high-risk tasks. In phase 3, the app exceeded success criteria for recruitment, retention, and satisfaction, with participants highlighting its usefulness, usability, and adoption. Qualitative feedback emphasized the app's practical value as a real-time resource, particularly its step-by-step guidance and user-friendly design. CONCLUSIONS: This study met its objectives, highlighting the SPH MSIP app's potential to address community health care workers' unmet safe patient handling needs and improve support in real time patient handling scenarios. While the findings suggest strong feasibility and acceptability, future research should focus on large-scale, extended effectiveness trials to evaluate the app's impact on reducing musculoskeletal injury rates and improving patient care outcomes.
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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".