A Salutogenic Approach to Understanding the Potential of Green Programs for the Rehabilitation of Young Employees With Burnout: Protocol for a Mixed Method Study on Effectiveness and Effective Elements
Bibliographic record
Abstract
BACKGROUND: Burnout is the leading cause of absenteeism in the Netherlands, with associated sick leave costs amounting to around €1.8 billion. Studies have indicated that burnout complaints increased from almost 14.4% in 2014 to 17.3% in 2018, especially among employees between the ages of 18 and 35 years, and further increases are expected. Although there are many published articles on burnout, not much is known about what constitutes effective rehabilitation (ie, the reduction of burnout complaints and the facilitation of returning to work). At the same time, multiple pilot studies have indicated that green programs are effective in both reducing burnout complaints and facilitating return to work. Green programs have been developed by professionals experienced in using the natural environment to facilitate rehabilitation (eg, through green exercise and healing gardens). The literature nevertheless lacks comprehensive and contextual insight into what works and why. OBJECTIVE: The overarching aim of this study is to explore the potential of green programs for young employees with burnout. We present the study protocol from an ongoing research project consisting of 2 phases, each composed of 2 research objectives that sequentially build upon each other. METHODS: The study is based on a sequential design with 4 research objectives, using both qualitative and quantitative research methods. In the first phase, a systematic literature review (research objective 1) and in-depth interviews (research objective 2) will be used to explore mechanisms underlying the rehabilitation of young employees with burnout. In the second phase, a multicase study will be conducted to examine the extent to which green programs are built on mechanisms identified in the first phase (research objective 3). By employing a pretest and posttest design, a specific green program that captures most of those mechanisms will then be evaluated on its effect and process with regard to the rehabilitation of young employees with burnout (research objective 4). The project started in June 2018 and will continue through June 2022. RESULTS: The first phase (research objectives 1 and 2) is intended to generate information on the mechanisms underlying the rehabilitation of young employees with burnout. The second phase (research objectives 3 and 4) is designed to demonstrate the extent to which and how the selected green program facilitates the rehabilitation of young employees with burnout. CONCLUSIONS: Understanding how green programs can facilitate the rehabilitation of young employees with burnout complaints can help to address this societal issue. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15303.
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 | 0.083 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".