Community-based palliative care needs and barriers to access among cancer patients in rural north India: a Participatory action research
Bibliographic record
Abstract
BACKGROUND: This paper aimed to explore the palliative care (PC) needs and barriers to access among cancer patients in a rural region of North India with a high cancer burden. METHODS: A Participatory action research (PAR) approach was employed. Situational assessment, community sensitization workshops (CSWs) and door-to-door surveys were planned, conducted and developed over three PAR cycles. A parallel convergent mixed-methods approach was adopted wherein the quantitative data from door-to-door surveys and qualitative data from CSWs and investigator field notes were collected and analyzed to provide a comprehensive understanding of PC needs and barriers to access. Descriptive statistics and thematic analysis were used. RESULTS: A total of 27 CSWs involving 526 stakeholders were conducted. A total of 256 cancer patients were assessed for PC needs and symptom burden using the Supportive and Palliative Care Indicators (SPICT-4ALL) and the Edmonton Symptom Assessment System (ESAS) tool, respectively. Based on the SPICT assessment, all patients (n = 256) satisfied general and/or cancer-specific indicators for PC. The majority (56.6%) had ≥ one moderate-severe symptom, with the most common symptoms being tiredness, pain and loss of appetite. Analysis of qualitative findings generated three themes: unmet needs, burden of caregiving, and barriers and challenges. Cancer affected all domains of patients' and their families' lives, contributing to biopsychosocial suffering. Social stigma, discrimination, sympathizing attitudes and lack of emotional and material support contributed to psychosocial suffering among cancer patients and their caregivers. Lack of awareness, nearby healthcare facilities, transportation, essential medicines, trained manpower and education in PC, collusion, fear of social discrimination, faulty perceptions and misconceptions about cancer made access to PC difficult. CONCLUSIONS: The study emphasize the need for and provide a roadmap for developing context-specific and culturally appropriate CBPC services to address the identified challenges and needs. The findings point towards education of CHWs in PC; improving community awareness about cancer, PC, government support schemes; ensuring an uninterrupted supply of essential medicines; and developing active linkages within the community and with NGOs to address the financial, transportation, educational, vocational and other social needs as some of the strategies to ensure holistic CBPC services. TRIAL REGISTRATION: Clinical Trial Registry of India (CTRI/2023/04/051357).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".