Communication and Relationships in Person Centered Medicine
Bibliographic record
Abstract
a Editor in Chief, International Journal of Person Centered Medicine; Secretary General, International College of Personcentered Medicine; Professor of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA. b President, International College of Person Centered Medicine; Former President, World Medical Association, London, United Kingdom. c Board Director, International College of Person-centered Medicine; Chair, World Psychiatric Association Section on Psychoanalysis in Psychiatry; Professor of Child and Adolescent Psychiatry, University of Western Brittany, Brest, France. d Board Director, International College of Person-centered Medicine; Former Officer, International Council of Nurses; Independent Consultant, Nursing and Health Policy, Alberta, Canada. e Board Director, International College of Person-centered Medicine; Former Chief Executive Officer, International Alliance of Patients' Organizations, London, United Kingdom. f Board Director, International College of Person-centered Medicine; Chair, Section on Classification, World Psychiatric Association; Professor of Psychiatry, University of Miami Miller School of Medicine, Miami, Florida, USA. g Board Director of the International College of Person-Centered Medicine and Professor of Communication in Healthcare at the Netherlands Institute for Health Services Research, Utrecht, the Netherlands; at the Department of Primary and Community Care at Radboud University Medical Center, Nijmegen, the Netherlands; and at the Faculty of Health Sciences, University College of Southeast Norway, Drammen, Norway.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".