Satisfaction levels among patients availing DOTS services in Bundelkhand Region (UP), India: Evidence from patient exit-interviews
Bibliographic record
Abstract
Context: Patient satisfaction is a determinant of treatment uptake and adherence to Directly Observed Treatment Short course (DOTS) therapy for Tuberculosis (TB). Waiting time, staff's attitude and improvement in symptoms may affect patient's satisfaction. Aims: The rationale of the present study is to assess the levels of satisfaction among patients utilizing the DOTS services and the factors contributing toward patient satisfaction. Settings and Design: Cross-sectional study. Study Duration: Jan 2009 to March 2009. Study Area: Four Designated Microscopic Centers (DMCs) of District Jhansi. Materials and Methods: Exit interviews of all the patients who were diagnosed as TB cases and put on DOTS in the first quarter of 2009 (i.e., subjects registered from 1 st January 2009 to 31 st March 2009) at four selected DMCs were taken on pre-tested questionnaire. Statistical Analysis: Results are expressed in percentages. Results: Average waiting time at center reported was 5-10 minutes by majority of cases (42.7%). Approx.78.6% patients were fully satisfied with the services provided at the centers. Lack of financial burden was the most common reason for satisfaction (95.4%), followed by improvement in symptoms (75%). Most common problems faced by the patients was difficult in coming on alternate days (30.0%) followed by loss of wages and transport charges (26.8%). Conclusion: The study findings could potentially be explored to improve DOTS services to attain maximum satisfaction among TB 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".