Editorial: The International Community of Practice for Person-centred Practice
Bibliographic record
Abstract
Welcome to this special issue of the International Practice Development Journal. The issue has been sponsored by the International Community of Practice for Person-centred Practice and all papers submitted went through the usual double-blind peer review as set out in the journal’s guidelines. Unsurprisingly, the topic of this special issue is research and scholarship around person-centred practice. \n \nTo set the scene, I offer you some background to the International Community of Practice for Person-centred Practice (PcP ICoP). It is an international community, mainly of academics who are interested in advancing knowledge in the field of person-centred practice. Note here that ‘practice’ is taken as being in any field: care, education, research, management, policy and so on. The ICoP is hosted by Queen Margaret University, Edinburgh and co-ordinated by Professor Brendan McCormack. \nPartners come from: \n\tUlster University, Northern Ireland \n\tFontys University of Applied Science, Netherlands \n\tBuskerud and Vestfold University College, Norway \n\tUniversity of Buffalo, United States \n\tUniversity of Technology/Sydney Children’s Hospitals Network, Australia \n\tWest Park Healthcare, Toronto, Canada \n \nThe Centre for Care Research at Bergen University College, Norway, is also about to join us. \nThe ICoP co-ordinates a programme of research and scholarship, and supports collaborative publications and presentations as well as a thriving community of practice for doctoral students who are studying aspects of person-centredness – the SICoP. Each of the partner organisations is engaged in teaching/learning, research and scholarship activities connected to person-centred practice – as you can go on to explore in the pages that follow. \n \nFinally, everyone at IPDJ and in the ICoP hopes that you find this special issue thought provoking, that it encourages conversation in your workplace and that it inspires you to ask yourself and others how person-centred practice can be more of reality for more people more of the time. \n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.021 |
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".