Knowledge, Attitudes, and Perceptions of Jordanians Toward Adopting and Using Telemedicine: National Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Due to the upsurge of COVID-19, nations are increasingly adopting telemedicine programs in anticipation of similar crises. Similar to all nations worldwide, Jordan is implementing efforts to adopt such technologies, yet it is far from complete. OBJECTIVE: This study aims to assess the knowledge, attitudes, and perceptions of Jordanians toward telemedicine, to identify key factors predisposing individuals to its use or acting as barriers to its implementation. METHODS: We implemented a cross-sectional design using an online, self-administered questionnaire executed in Google Forms and distributed through social media. Differences in knowledge and attitude scores were examined using independent sample t tests and ANOVA. A multivariate linear regression model was computed to assess predictors of awareness toward telemedicine. RESULTS: A total of 1201 participants fully completed the questionnaire. Participants were characterized by a mean age of 36.3 (SD 14.4) years and a male-to-female ratio of nearly 1:1. About 50% (619/1201, 51.5%) of our studied population were aware of telemedicine, while nearly 25% (299/1201, 24.9%) declared they had observed it in action. Approximatively 68% (814/1201, 67.8%) of respondents were willing to use telemedicine. The majority of the sample portrayed favorable and positive views toward telemedicine. Higher educational degrees, living in urban districts, and having a higher perception of electronic usage ability were associated with higher knowledge and better attitudes toward telemedicine (all P<.05). The multivariate linear regression analysis demonstrated that perceived ability to use electronics was associated with positive attitudes (β=0.394; 95% CI 0.224 to 0.563), while living in Southern Jordan predicted poor attitudes toward telemedicine (β=-2.896; 95% CI -4.873 to -0.919). CONCLUSIONS: Jordanians portray favorable perceptions of telemedicine. Nonetheless, concerns with regards to privacy, medical errors, and capacity for accurate diagnoses are prevalent. Furthermore, Jordanians believe that integrating telemedicine within the health care system is not applicable due to limited resources.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".