End-of-life issues, grief, and bereavement : what clinicians need to know
Bibliographic record
Abstract
Contributors. Preface. 1 Introduction to End-of-Life Care for Mental Health Professionals (Julia E. Kasl-Godley). 2 Trajectories of Chronic Illnesses (Michelle S. Gabriel). 3 The Cultural Context of Spirituality and Meaning (E. Alessandra Strada). 4 Working With Family Caregivers of Persons With Terminal Illness (David B. Feldman and Jasmin Llamas). 5 Serious Mental Illness (Julia E. Kasl-Godley). 6 Advance Care Planning (Michelle S. Gabriel and Sheila Kennedy). 7 Pharmacologic Management of Pain (W. Nat Timmins). 8 Nonpharmacological Approaches to Pain and Symptom Management (Stephanie C. Wallio and Robert K. Twillman). 9 Grief and Bereavement Care (Shirley Otis-Green). 10 Complicated Grief (E. Alessandra Strada). 11 Health-Care Teams (Julia E. Kasl-Godley and Donna Kwilosz). 12 End-of-Life Care in Long-Term Care Settings (Mary M. Lewis). 13 Advocating for Policy Change: The Role of Mental Health Providers (Robert K. Twillman and Mary M. Lewis). 14 Physician-Assisted Suicide in the United States: Issues, Challenges, Roles, and Implications for Clinicians (Silvia Sara Canetto). 15 Creating Ethics Conversations in Community (Malham M. Wakin). 16 Professional Self-Care (E. Alessandra Strada). 17 Embracing the Existential Invitation to Examine Care at the End of Life (Shirley Otis-Green). Author Index. Subject Index.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".