Bibliographic record
Abstract
Abstract Dignity in Care: The Human Side of Medicine brings together decades of clinical experience and rigorous research from one of the world’s leading authorities on palliative care and dignity in the healthcare setting. Dr. Harvey Max Chochinov, a seasoned psychiatrist, healthcare leader, and prolific researcher, has distilled his insights about caring for patients and achieving dignity in care for anyone facing the need for healthcare. This book has been written for everyone who deals with patients as part of their work, be they doctors, nurses, social workers, hospital chaplains, occupational therapists, physiotherapists, physician’s assistants, healthcare aides, x-ray technologists, radiation therapists, respiratory therapists, pharmacists, medical receptionists, or healthcare trainees of any kind. Dignity in Care: The Human Side of Medicine explains why patients respond as they do and how the disposition and attitude of healthcare providers indelibly shapes patient experience. The text details the various components of optimal therapeutic communication and offers ways of understanding and delivering dignity in care. This book, already hailed as “a must read for all healthcare providers,” is chock full of wonderful stories and impeccable evidence designed to move healthcare providers—and those they care for—toward the human side of medicine.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".