Bibliographic record
Abstract
PART I: INTRODUCTION 1. Applying a public health perspective in end-of-life care PART II: CLINICAL AND SOCIAL CONTEXT OF DEATH AND DYING 2. Place of death and end-of-life care 3. Circumstances of death and dying 4. End-of-life decisions 5. Economic and health related consequences of individuals caring for terminally ill cancer patients in Canada PART III: END-OF-LIFE CARE: PROVISION, ACCESS, AND CHARACTERISTICS 6. Aggressive treatment and palliative care at the end of life 7. Access to palliative care 8. Communication between patient and caregiver PART IV: END-OF-LIFE CARE SETTINGS 9. Palliative care in primary care 10. Palliative care in institutional long-term care settings 11. Palliative care in hospitals PART V: INEQUALITIES AT THE END OF LIFE: UNDERSERVED GROUPS 12. Non-cancer patients 13. Palliative care for the older adult 14. A public health framework for pediatric palliative and hospice care 15. End-of-life care for patients with intellectual disabilities 16. End-of-life care for people who live in rural or remote areas versus those who live in urban areas 17. Social inequalities at the end of life PART VI: END-OF-LIFE CARE POLICIES 18. Design, implementation, and evaluation of palliative care programs and services with a public health who perspective 19. Public health policy regarding end-of-life care in sub-Saharan Africa 20. Palliative care in the global context: understanding policies to support end-of-life care 21. The importance of family carers in end-of-life care: a public health approach PART VII: CONCLUSION 22. Conclusions
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".