Respite Model of Palliative Care for Advanced Cancer in India: Development and Evaluation of Effectiveness
Bibliographic record
Abstract
Palliative care need is highest among low and middle-income countries and a significant gap exists between palliative care need and provision in India. Patients with life limiting illnesses such as cancer are discharged home prematurely with poorly controlled physical symptoms, unresolved psychological and social issues, and unprepared caregiver and with no plans for continuity of care. Respite model of palliative care is a step down from acute setting, where patients are admitted briefly to a respite home for symptom control, and management of psychosocial issues,caregiver empowerment, liaison and networking with local family physician and local palliative care network and planned discharge home. The effectiveness of the project will be measured using four parameters (a) improvement in symptoms using Edmonton symptom assessment scale (ESAS) (b) quality of life using EORTC QLQ-PAL15C (c) family satisfaction using selected items of FAMCARE questionnaire and d. care giver empowerment using a semi structured questionnaire. This project is expected to benefit around 1500 adult palliative care patients and 150 pediatric palliative care patients. The research article highlights needs assessment, concept, project development, research design and proposed impact of the project.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".