Improving Work-Related Challenges in Psychiatric-Psychosomatic Clinics: Study Protocol for an Internet-Based Needs Assessment and Co-Design of a Training
Bibliographic record
Abstract
Background: Medical, psychiatric-psychosomatic facilities are confronted with a variety of daily challenges that affect working conditions, the mental health of employees, and the quality of patient care. This project focuses on the work-related challenges faced by health care professionals in psychiatric-psychosomatic clinics in Germany. Objective: The aim of the current research is to investigate the interactions between individuals and their social environment, identify psychological and organizational challenges and job demands, and use these findings to inform the development of a participatory, evidence-based intervention. Methods: This 2-phase research is grounded in the job demands-resources model (JD-R). Study phase (needs assessment) uses a cross-sectional online survey with health care professionals in German psychiatric-psychosomatic clinics to assess job demands, resources, and outcomes in a target sample of N=600 participants (power analysis). Study phase 2 (co-design of a training) involves co-creatively designing an intervention based on survey findings through participatory workshops with at least N=20 participants. Analyses include regression and moderation tests (SPSS; IBM Corporation) and qualitative data analysis to co-design training. Results: The recruitment of participants is planned to be finished by December 2025. The co-designing of workshops (phase 2) will be started in February 2026. As this is a study protocol, results are not available yet. Conclusions: This current research examines the work-related challenges faced by health care professionals in psychiatric-psychosomatic clinics. It is expected that burnout, engagement, and psychological safety will likely emerge as central mediating and moderating variables. As the findings of phase 1 serve as a basis for the development of an intervention, this research seeks to improve the well-being of health care professionals in psychiatric-psychosomatic institutions sustainably.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".