The Long Way Toward Cooperation
Bibliographic record
Abstract
To better understand why cooperation between health care professionals is still often problematic, we carried out 25 semistructured face-to-face expert interviews with physicians and nurses in different rural and urban areas in northern Germany. Using Mayring's qualitative content analysis method to analyze the data collected, we found that doctors and nurses interpreted interprofessional conflicts differently. Nursing seems to be caught in a paradoxical situation: An increasing emphasis is placed on achieving interprofessional cooperation but the core areas of nursing practice are subject to increasing rationalization in the current climate of health care marketization. The subsequent and systematic devaluation of nursing work makes it difficult for physicians to acknowledge nurses' expertise. We suggest that to ameliorate interprofessional cooperation, nursing must insist on its own logic of action thereby promoting its professionalization; interprofessional cooperation cannot take place until nursing work is valued by all members of the health care system.
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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 | 0.058 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.071 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".