Knowledge comparison amongst telehealth service utilized and never-utilized adults in Bangladesh: a cross-sectional study
Bibliographic record
Abstract
Background: Telehealth service is an approach to health care delivery that uses various telecommunication technologies, where the knowledge of the patient plays an important role in its acceptance, preference, and utilization. This study compared telehealth service knowledge among adults who utilized and never-utilized telehealth services and explored factors associated with telehealth knowledge. Methods: This comparative cross-sectional study recruited 1252 adults from Bangladesh. The outcome variable of the study was the knowledge of telehealth services. We used convenience sampling approaches to recruit participants. The online questionnaire was distributed via Google Forms through Facebook, Messenger, and WhatsApp. The independent variables of the study were sociodemographic factors and the perceived health status of the participants. The bivariate logistic regression model was used to investigate the association between study variables and the level of knowledge among those who utilized and never-utilized the telehealth service. The data analysis was done using STATA version 16. Results: In the never-utilized group, 54.41% of participants were male, with an average age of 28.89 years. In the utilized group, 55.77% of the participants were male, with an average age of 30 years. Age, marital status, educational level, student status, and perceived health status were significantly associated with good telehealth knowledge among those who never-utilized the telehealth service. Among the utilized groups, we found that age, marital status, and perceived health status were significantly associated with good knowledge of telehealth services. Conclusions: This study emphasizes the importance of addressing the associated factors to improve telehealth knowledge, considering existing variations among adults who utilized and who never-utilize telehealth services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".