IJCM_200A: Symptom Burden Profile of individuals facing chronic life-limiting illnesses and barriers to effective management of symptoms in palliative care- Grounded theory qualitative study.
Bibliographic record
Abstract
Background: Managing symptoms such as pain, breathlessness, and fatigue poses significant challenges in patients with advanced diseases. While palliative care has proven to enhance symptom control, there is a lack of exploration into the factors that contribute to successful symptom management in this specific context. This study was conducted to gain insight into the elements that support effective symptom management in palliative care within home settings and to explore the barriers that are encountered in this process. Objectives: To enumerate symptom burden profile of Patients facing chronic life limiting illness registered in Home based Palliative care program implemented by SVYM in Bengaluru and Hassan cities . Methodology: This is a Gounded theory study using qualitative semi- structured interviews. conducted between August 2023 to December 2023. Study participants are the primary caregivers of Patients with chronic life limiting illness in need of palliative care in Hassan and Bengaluru. 50 participants were interviewed through convenience sampling for the purpose of the study. Edmonton's Symptom assessment scale (ESAS) was used to assess the symptom burden of patient during the registration and 3 weeks post intervention. Conclusion: Effective symptom management relies on collaborative decision-making between patients and healthcare professionals, along with the coordinated efforts of a multidisciplinary team. Taking steps to address psychological distress and assessing the comprehension and expectations of patients and their families would enhance the overall effectiveness of symptom management in palliative care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".