Decentralized worker-centred occupational management in health care: nationwide survey and alpha testing
Bibliographic record
Abstract
BACKGROUND: Occupational stress among healthcare workers negatively impacts job satisfaction and patient care quality, jeopardizing healthcare system sustainability. Traditional employer-driven approaches often fail to address these challenges comprehensively, leading to persistent gaps in work condition transparency and well-being. AIMS: To elucidate the working conditions of health workers and introduce a worker-centred, technology-based strategy moving beyond traditional practices and entrenched medical culture. METHODS: A nationwide survey of Belgian medical residents evaluated occupational conditions and perceptions of management practices. Additionally, the alpha version of a decentralized mobile application was tested to gather user satisfaction and feedback on its usability. The data were surveyed using Pearson's chi-squared and Kruskal-Wallis rank sum tests to assess associations between categorical and ordinal variables, respectively. Alpha-testing results were evaluated using descriptive statistics. RESULTS: The nationwide survey, involving 257 participants, revealed significant associations between medical specialty, work choices and compensation. Notably, 91% of participants expressed strong interest in our proposed open, decentralized solution. In the alpha testing phase, 12 testers reported high satisfaction regarding time-tracking accuracy and payroll verification, though challenges related to administrative burden were also identified. CONCLUSIONS: The findings underscore the need for innovative, worker-centred occupational management solutions. The proposed solution shows promise in improving autonomy and transparency, potentially enhancing healthcare delivery and sustainability.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".