Acceptability of the eHealth Intervention Sustainable Worker Digital Support for Persons With Chronic Pain and Their Employers (SWEPPE): Questionnaire and Interview Study
Bibliographic record
Abstract
BACKGROUND: Sick leave and decreased ability to work are the consequences of chronic pain. Interdisciplinary pain rehabilitation programs (IPRPs) aim to improve health-related quality of life and participation in work activities, although implementing rehabilitation strategies at work after IPRPs can be difficult. Employers' knowledge about pain and the role of rehabilitation needs to be strengthened. The self-management of chronic pain can be improved through eHealth interventions. However, these interventions do not involve communicating with employers to improve work participation. To address this deficiency, a new eHealth intervention, Sustainable Worker Digital Support for Persons with Chronic Pain and Their Employers (SWEPPE), was developed. OBJECTIVE: This study aimed to describe the acceptability of SWEPPE after IPRPs from the perspective of patients with chronic pain and their employers. METHODS: This study included 11 patients and 4 employers who were recruited to test SWEPPE in daily life for 3 months after IPRPs. Data were collected using individual interviews at the end of the 3-month test period and questionnaires, which were completed when SWEPPE was introduced (questionnaire 1) and at a 3-month follow-up (questionnaire 2). Data were also collected on how often SWEPPE was used. Qualitative data were analyzed through a qualitative content analysis using an abductive approach. The framework used for the deductive approach was the theoretical framework of acceptability. Quantitative data were analyzed through descriptive statistics and the differences between the responses to questionnaires 1 and questionnaire 2 using the Wilcoxon signed rank test. RESULTS: Both patients and employers reported that SWEPPE increased their knowledge and understanding of how to improve work participation and helped them identify goals, barriers, and strategies for return to work. In addition, participants noted that SWEPPE improved employer-employee communication and collaboration. However, experiences and ratings varied among participants and the different SWEPPE modules. The acceptability of SWEPPE was lower in patients who experienced significant pain and fatigue. A high degree of flexibility and choice of ratings in SWEPPE were generally described as helpful. CONCLUSIONS: This study shows promising results on the user acceptability of SWEPPE from both patient and employer perspectives. However, the variations among patients and modules indicate a need for further testing and research to refine the content and identify the group of patients who will best benefit from SWEPPE.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".