Experiences of End-of-Life Care of Older Adults with Cancer From the Perspective of Stakeholdersin Iran: A Content Analysis Study
Bibliographic record
Abstract
OBJECTIVES: To describe end-of-life care forolder adults with cancer admitted to the hospital in Tehran, Iranto determine if there were any gaps in care for older adultsthat can be improved. MATERIALS: This study used a qualitative descriptive study design. In total, 37 individualsincluding patients, healthcare team members, and family caregivers, participated in the study. Semi-structured interviews using topic guides were conducted, and the thematic content analysis method described by Braun and Clarke (2006), was used to analyze the data. RESULTS: In total, 37 Iranian participants (12 male and 25 female), including 14 nurses, 3 oncologists, 1 social worker, 1 chaplain, 1 psychologist, 11 family members and 6 patientsinterviewed.Our main themes of end-of-life carewere:1) barriers to providing and receiving quality care for families and patients; and 2) coping strategies and empowerment of families and patients. CONCLUSION: Healthcare providers are recommended to familiarize themselves with the burden faced by patients and family caregivers who take care of older adults with chronic diseases at home, and they should organize their supportive and consulting actions. In order to improve the quality of life of older patientsand their family caregivers.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Qualitative study of end-of-life cancer care in Iran.
This is a qualitative study of end-of-life cancer care in Iran, not research itself.
Content analysis of end-of-life cancer care delivery in Iran; clinical/health services domain.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".