Service Quality Of Diagnostic Fine Needle Aspiration Cytology In A Tertiary Care Hospital Of Lahore (Process Measure As Patient's Perspective).
Bibliographic record
Abstract
BACKGROUND: Quality of any service is the most important aspect for the manufacturer as well as the consumer. The primary objective of any nation's health system is to provide supreme quality health care services to its patients. The objective of this study was to assess the quality of diagnostic fine needle aspiration cytology service in a tertiary care hospital. As Patient's perspectives provide valuable information on quality of process, therefore, patient's perception in terms of satisfaction with the service was measured. METHODS: In this cross sectional analytical study, 291 patients undergoing fine needle aspiration cytology in Mayo Hospital were selected by systematic sampling technique. Information regarding satisfaction of patients with four dimensions of service quality process, namely "procedure, sterilization, conduct and competency of doctor" was collected through interview on questionnaire. The questionnaire was developed on SERVQUAL model, a measurement tool, for quality assessment of services provided to patients. All items were assessed on 2- point likert scale (0=dissatisfied, 1=satisfied). Frequencies and percentages of satisfied and dissatisfied patients were recorded for each item and all items in each dimension were scored. If the percentage of sum of all item scores of a dimension was ≥60, the dimension was 'good quality'. Whereas <60% was 'poor quality' dimension. Data was analysed using epi-info-3.5.1. Fisher test was applied to check statistical significance. (p-value <0.05). RESULTS: Out of the 4 dimensions of service quality process, Procedure (48.8%), Sterilization (51.5%) and practitioner conduct (50.9%) were perceived as 'poor' by the patients. Only practitioner competency (67.4%) was perceived as 'good'. Comparison of dimensions of service quality scoring with overall level of patient satisfaction revealed that all 4 dimensions were significantly related to patient dissatisfaction (p<.05). CONCLUSIONS: The study suggests that service quality of therapeutic and diagnostic procedures in public hospitals should be routinely monitored from the patients' point of view as most aspects of service quality in public hospitals of Pakistan, require improvements. In this manner patient's satisfaction regarding use of services in public hospitals can be made better.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".