Exploring Drivers of Work-Related Stress in General Practice Teams as an Example for Small and Medium-Sized Enterprises: Protocol for an Integrated Ethnographic Approach of Social Research Methods
Bibliographic record
Abstract
BACKGROUND: An increasing shortage of skilled personnel, including medical personnel, has been reported in many postindustrial economies. Persisting and growing trends in absenteeism and incapacity to work due to mental disorders are concerning and have increased political, economic, and scientific interest in better understanding and management of determinants related to the work environment and health. OBJECTIVE: This study protocol describes an integrated approach of social research methods to explore determinants of work-related stress in general practice teams as an example for micro, small, and medium-sized enterprises (SMEs). METHODS: The methods applied will allow an in-depth exploration of work practices and experiences relating to psychological well-being in general practice teams. An ethnographic approach will be used to develop an in-depth understanding of the drivers of work-related stress in general practice teams. We will combine participating observation and individual interviews with five to seven general practitioners (GPs), and five to seven focus group discussions with the nonphysician staff (3-4 participants per group) in approximately four GP group practices and one single practice in Germany. Data collection and analysis will follow a grounded theory approach. RESULTS: The Ethics Committee of the Medical Faculty, University Hospital of Tuebingen, Germany, has approved this study (reference number: 640/2017BO2). Recruitment has commenced with study completion anticipated in mid-2020. CONCLUSIONS: The data from this project will be used in follow-up projects to develop and test an intervention to reduce and prevent work-related stress in GP practices and other SMEs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15809.
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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 | 0.057 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 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".