Home-based Supportive Care Model for Bedridden Patients: A Primary Health Care Approach in Rural Ballabgarh, Haryana, India
Bibliographic record
Abstract
BACKGROUND: Bedridden patients heavily rely on caregivers for daily living activities and accessing care. They have the issues not only with physical health but also psychosocial and spiritual health. OBJECTIVES: This study implemented a home-based supportive care model based on primary healthcare approach for bedridden patients and assessed its feasibility and effect. MATERIALS AND METHODS: This model was implemented at a primary health center in rural Ballabgarh, Haryana. Health workers identified the bedridden patients, and medical interns assessed their concerns across physical, mental, social, and spiritual domains. Individual care plans were developed after family meetings, including caregiver training. Health workers conducted monthly home visits for medication refills and supportive care. Baseline and 3-month follow-up assessments used Edmonton symptom assessment scale-revised (ESAS-r) and distress thermometer to assess effect. Feedback was collected from patients, caregivers, and health workers. RESULTS: Of the 74 identified bedridden patients, 71 were enrolled. The mean age was 52.8 years, with a median bedridden duration of 6.1 years. The common symptoms included pain (91.7%), sleep-related issues (60.4%), and tiredness (56.3%). Postintervention, significant reductions were observed in distress scores (median score reduced from 6 to 4.5, P <0.05), pain (median score 5 to 4, P -value<0.05), tiredness (median score 2 to 0.5, P -value < 0.05), and depression (median score 1.5 to 0, P -value <0.05) on ESAS-r. Feedback from health workers and interns highlighted increased self-confidence, compassion for others, and gained respect in the community. CONCLUSION: This model of home-based supportive care was feasible and effective in reducing the symptoms and distress among bedridden patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".