Could a feedback device help manage work-related shoulder disorders?: protocol of a mixed methods pilot study
Bibliographic record
Abstract
BACKGROUND: Work-related shoulder disorders (WRSDs) significantly impact workers' health and well-being. Various factors contribute to WRSDs, including work environment, physical workload, psychological stressors, and an aging workforce. Despite numerous interventions proposed to address WRSDs, finding high-quality evidence to support any of them remains challenging. One potential reason is the lack of valid tools to measure physical workload and customize interventions. To address this, we developed a portable feedback system, the ShoulderWatchLoad (SWL), designed to estimate physical work demands on the shoulder. The objectives of this pilot study are (a) to evaluate the feasibility of implementing the SWL in the workplace and conducting a large-scale randomized clinical trial (RCT) by assessing recruitment success, intervention adherence, usability of the technology, and the research team's capacity to refine the SWL and (b) to explore the SWL capacity to reduce disability, pain, work limitations, and physical work demands while increasing pain self-efficacy among workers with WRSDs. METHODS/DESIGN: A convergent mixed methods pilot study will be conducted. The quantitative component will consist of a two-arm parallel group external pilot RCT involving 42 participants with WRSDs who will be randomized into one of the two study groups. Following baseline evaluation, all participants will attend an education session in the workplace. Following this session, participants of the experimental group will receive a 2-week workplace intervention using feedback from the SWL, while participants in the control group will not receive this intervention. All participants will undergo four evaluations over a 12-week period (baseline, 3rd, 6th, and 12th weeks). The qualitative component will be a interpretative descriptive qualitative study involving three focus groups with 21 participants from the experimental group. An innovative joint display will be used to represent the integration of qualitative and quantitative data, along with progression criteria, to draw meta-inferences about the feasibility of implementing the SWL in the workplace and conducting a full-scale RCT in the future. DISCUSSION: A holistic approach combining qualitative and quantitative elements will enable us to determine the SWL usability and whether the conduct of a full RCT is feasible. TRIAL REGISTRATION: ClinicalTrials.gov NCT06693479.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".