Trustworthiness of Web-Based Pharmacy Apps in Pakistan Based on the Mobile App Rating Scale: Content Analysis and Quality Evaluation
Bibliographic record
Abstract
Background: Web-based pharmacy apps facilitate the electronic exchange of health-related supplies. They are digital platforms that run on websites and smartphones. Pakistan is experiencing significant progress in smartphone integration and digital services, leading to the expansion of the online pharmacy business. However, concerns remain over the legitimacy and precision of these apps. Objective: The aim of this study was to undertake a thorough assessment of digital pharmacy apps accessible in Pakistan. Specifically, our focus was on apps accessible via the Google Play Store and the iOS App Store. To fulfill this objective, an evaluation of these apps was performed using the Mobile App Rating Scale (MARS). Methods: A research investigation was conducted to analyze the online pharmacy apps in Pakistan. Initially, 50 apps were identified, but 10 were excluded for not meeting pre-established criteria, 10 were excluded for being in languages other than English, and 7 could not be downloaded. All paid and non-English apps were also excluded. A total of 23 apps were selected for the study, acquired via the Google Play Store and iOS App Store. The evaluation was conducted by 2 researchers who maintained independence from one another by using the MARS. Results: Initially, 50 apps were identified, of which 27 were excluded for not meeting the predetermined criteria. A total of 23 apps were selected for the study, acquired via the Google Play Store and iOS App Store. Strong positive correlations between higher user engagement and better app functionality and information quality were observed. The average rating of the 23 apps ranged between 2.64 and 4.00 on a scale up to 5. The aesthetics dimension had the highest mean score of 3.6, while the information dimension had the lowest mean score of 3.2. For credibility and reliability, different tests (intraclass correlation, Cohen κ, Krippendorff α, and Cronbach α) on each dimension of the MARS were performed by using SPSS Statistics 27. The intraclass correlation of all MARS dimensions ranged from 0.702-0.913 (95% CI 0.521-0.943), the Cohen κ of all MARS dimensions ranged from 0.388-0.907 (95% CI 0.151-0.994), the Krippendorff α of all MARS dimensions ranged from 0.705-0.979 (95% CI 0.657-0.923), and Cronbach α had a lower score of 0.821 in the information dimension and a higher score of .911 in the subjective quality dimension of the MARS. Conclusions: This study evaluated online pharmacy apps in Pakistan by using the MARS. It is the first study on online pharmacy apps in Pakistan. The findings of the evaluation have provided insights into the reliability and efficacy of these apps.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".