Satisfaction of patients receiving value added-services compared to traditional counter service for prescription refills in Malaysia
Bibliographic record
Abstract
BACKGROUND: Patients' satisfaction is the key parameter to measure the quality of healthcare services. Value added-services (VAS) were introduced to improve the quality of medication deliveries and to reduce the waiting time at outpatient pharmacy. OBJECTIVE: This study aimed to compare the satisfaction levels of patients receiving VAS and traditional counter service (TCS) for prescription refills in Port Dickson Hospital. METHODS: A single-center, cross-sectional study was conducted in the outpatient pharmacy department of Port Dickson Hospital from 1 March to 30 June 2017. Systematic sampling method was utilized to recruit subjects into the study, except mail pharmacy in which universal sampling method was used. Data collection was done via telephone interviews for both groups. RESULTS: There was 104 and 105 in TCS and VAS group respectively. The response rate was 99.5%. Overall, a significant higher total mean satisfaction score in VAS group was observed as compared to TCS group (43.39 versus 40.49, p=0.002). The same finding was observed after confounding factors were controlled (VAS=44.66, 95% CI 43.07:46.24 versus TCS=39.88, 95% CI 38.29:41.46; p<0.001). VAS respondents reported more satisfaction than TCS respondents for both general and technical aspects. Among the VAS offered, mail pharmacy service respondents showed highest total mean satisfaction score, but no significant different was seen between groups (p=0.064). CONCLUSION: VAS respondents were generally more satisfied than TCS respondents for prescription refills. A longitudinal study is necessary to examine the impact of other dimensions and other types of VAS on patients' satisfaction levels.
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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 | 0.000 | 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) | 0.003 | 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".