Parabeln der Pflege. Kreative Reaktionen in der Demenzpflege, von Pflegenden erzählt [Parables of Care German version]
Bibliographic record
Abstract
German version of Parables of Care (2017). Translated into German by Dr Andrea Hacker. Parables of Care presents true stories of creative responses to dementia care, told by carers, taken from a group of over 100 case studies available at http://carenshare.city.ac.uk/. Creativity, emotional intelligence and common sense are amply shown in these 14 touching and informative stories. Drawn by Dr Simon Grennan with Christopher Sperandio. Edited and adapted by Dr Simon Grennan, Dr Ernesto Priego and Dr Peter Wilkins. Created with funding from City, University of London's MCSE School Impact Fund 2017, the University of Chester, UK and Douglas College, Vancouver, Canada. Diese 14 rührenden und informativen Geschichten zeigen viel Kreativität, Einfühlsamkeit und gesunden Menschenverstand. Parabeln der Pflege präsentiert wahre Geschichten über kreative Reaktionen in der Demenzpflege, die von Pflegenden erzählt wurden und aus einer Sammlung von über 100 Fallstudien in Großbritannien ausgewählt wurden. Diese englischsprachigen Fallstudien stehen auf http://carenshare.city.ac.uk zur Verfügung. Dies ist ein Projekt des Centre for Human Computer Interaction Design, City, der Universität London und der Universität Chester in Großbritannien, sowie des Douglas College in Vancouver, Kanada.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.017 |
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".