Psychosocial Resources in Health Care Systems
Bibliographic record
Abstract
Jobs and organizations are changing more and more rapidly. These changes go along with increasing demands, job insecurity, and feelings of stress and overload. Although all economic sectors are confronted with such drastic changes and negative consequences, this book shows that there are of a special nature in the human service industry. However, at the same time, many changes also pose challenges and produce resources that may cause learning, growth, and development. In addition to the more traditional view that focuses on the negative effects of organizational change, this book also emphasizes the potential positive aspects. Since 1985 the European Network of Organizational Psychologists (ENOP) has initiated a series of conferences in the field of health care. The IX conference took place in October 2005 in Dresden. Traditionally, job stress factors and mental health have been the main topics in earlier conferences but since the turn of the century a positive approach emerged that focused on resources instead of demands and on well-being instead of stress. Therefore, the Dresden conference focused on “Psychological Resources in Human Service Work”. This volume includes 15 contributions from authors of 9 countries from Europe and Canada. The contributions are structured in three sections. The first section includes chapters about work conditions and organizational changes in human service work, especially in nurses and teachers. The second section deals with organizational and emotional stress factors, and well-being. The final section includes chapters about knowledge work in health care and competence training.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".