Users’ Perceived Service Quality of National Telemedicine Services During the COVID-19 Pandemic in Bangladesh: Cross-Sectional Study
Bibliographic record
Abstract
Background: COVID-19 created an opportunity for using teleconsultation as an alternative way of accessing expert medical advice. Bangladesh has seen a 20-fold increase in the use of teleconsultation during the pandemic. Objective: The aim of our study was to assess the influence of service quality and user satisfaction on the intention to use teleconsultation in the future among users of national teleconsultation services during the pandemic. Methods: A cross-sectional survey was conducted in 2020 among users of the national teleconsultation service-Shastho Batayon for acute respiratory infection. A validated mobile health service quality model based on structural equation modeling and confirmatory factor analysis was used to analyze the data with SmartPLS (version 3.0). Results: Among the 2097 study participants, 1646 (78.5%) were male, 1416 (67.5%) were aged 18-39 years, 1588 (75.7%) were urban residents, 1348 (64.2%) had more than 10 years of schooling, and 1657 (79%) were from middle-income households. From a consumer perspective, the quality of the service platform (β=.946), service interaction (β=.974), and outcome (β=.955) contributed to service quality. Service quality was positively associated with user satisfaction (β=.327; P<.001) and intention to use teleconsultation services (β=.102; P<.001). User satisfaction was positively associated with the intention to use teleconsultation services (β=.311; P<.001). Conclusions: The increase in the use of teleconsultation during the pandemic indicated that such services were potentially used for emergencies. However, the future use of teleconsultation will be dependent on the quality of service and user satisfaction. Our findings are relevant for low-income contexts where teleconsultation services are used to address gaps in service delivery.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".