FACTORS AFFECTING ADHERENCE TO MEDICAL APPOINTMENT AMONG HYPERTENSIVE PATIENTS OF PUBLIC HEALTH FACILITIES IN PUNJAB, INDIA
Bibliographic record
Abstract
Objective: Objective: Regular visit to health facilities is important for hypertension control. Under India Hypertension Control Initiative, hypertensive patients are enrolled in public hospitals and provided with patient-specific monthly medication for regular treatment at home and advised to return to the health facility after a month. However, at least 50% -60% of registered patients were not visiting the health facility for 3 to 6 months following their last visits. This article aims to document the major reasons for missed appointments and to characterize the patient and health system barriers. Design and method: We used a telephonic interview for collecting information from the patients who missed the appointments for more than 3 consecutive months. Out of 300 randomly selected patients, 206 were interviewed with a pre-structured questionnaire to explore patients’ experiences along with medical record reviews from the patient database. Interviews were conducted during the last quarter of 2019 and analyzed in both descriptive statistics and textual descriptions. Results: Not feeling sick or not experiencing any bodily symptoms due to hypertension (18.4%) followed by far distance between the health facility and the patient's home (16.5%) were the major reasons reported by the patients. Among other reasons, lack of proper instructions/guidance from the facility (11%), acute health conditions of patients (6%) and long waiting time at the public health facilities (5%) are also documented as the reasons for poor follow up. These factors cumulatively shifting a patient towards private sectors as 54% patients who were continuing their treatment are preferring private sectors. Conclusions: The results suggest the need for a more patient-centric care model such as guiding the patient properly at the health facility, saving patients time in the clinic, personalized care for acutely ill and aged patient and addressing the distance barriers between a patient's home and the health facility. Further, introducing reminder system by means of phone calls/SMS/visit may help to retain the higher follow-up of the patient.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".