Development of a Web-Based Acceptance and Commitment Therapy Intervention to Support Lifestyle Behavior Change and Well-Being in Health Care Staff: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: Positive emotional well-being is associated with healthier lifestyle choices and overall health function, whereas poor mental health is associated with significant economic and psychological costs. Thus, the development of effective interventions that improve emotional well-being is crucial to address the worldwide burden of disease. OBJECTIVE: This study aims to develop a web-based emotional well-being intervention for use by health care staff using participatory design to consider adherence and engagement from a user perspective. METHODS: A 3-staged iterative participatory design process was followed, including multiple stakeholders: researchers, computer scientists, mental health experts, and health care staff. Stage 1 used document analyses, direct observation, and welcome interviews; stage 2 used focus group discussions, rapid prototyping, and usability tasks; and stage 3 evaluated a high-fidelity prototype. RESULTS: Different health care staff (N=38) participated during a sustained period. A structured, sequential, automated, 12-week, web-based emotional well-being intervention based on acceptance and commitment therapy was developed. Freely navigated psychoeducational resources were also included. CONCLUSIONS: The iterative and collaborative participatory design process successfully met its objectives. It generated an in-depth understanding of well-being within the workplace and identified barriers to access. The 3-staged process ensured that participants had the opportunity to explore and articulate criteria relevant to their roles over time and reflect on decisions made at each stage.
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 | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".