Satisfaction with Healthcare Services Provided in Public Hospitals of Southern Punjab, Pakistan: Study of District Head Quarter Hospitals
Bibliographic record
Abstract
The purpose of this research is to explore patient complaints and patient satisfaction in the context of District Head Quarter Hospitals (DHQHs) practice improvement. The objectives of the study are to evaluate health-related quality and facilities of services, patient satisfaction, and adherence to treatment in patients with moderate in District Head Quarter Hospitals (DHQHs) Southern Punjab, Pakistan. The methodology used was empirical, quantitative and data were represented in percentage tables. Primary data was collected through the questionnaires from each district of Southern Punjab. The targeted population was patients and attendants of DHQHs. The random sampling technique was used for the collection of data. Closed ended and Likert scale questionnaires was entertained for data collection. The sample size was 100 and the response rate was cent percent after follow-up. Data is analyzed through regression and correlation by using SPSS software. The findings shows that the main factor that highly influenced on the satisfaction of patient and cause dissatisfaction of DHQHs services is the attitude of doctors, lab-technicians, nurses and clerical staffs.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".