Perceived Knowledge, Attitudes, and Barriers Toward the Adoption of Telemedicine Services in the Kingdom of Saudi Arabia: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The adoption of telemedicine has facilitated the opportunity to offer the best health care services. Nevertheless, there is a mismatch between the presence of telemedicine programs in the Kingdom of Saudi Arabia and poor acceptance by the end-user patients. OBJECTIVE: This study aimed to gain a holistic understanding of the knowledge, attitudes, and barriers of the end-user patients (ie, research participants) toward the utility of telemedicine services in the Kingdom of Saudi Arabia. METHODS: A cross-sectional, survey-based study was conducted from June 1 to July 31, 2022, in the Kingdom of Saudi Arabia. The questionnaire was developed based on a literature review and was examined for validity and reliability. Knowledge questions used a yes-or-no format, whereas attitude and barrier questions used a 5-point Likert-scale format. Data were reported descriptively and analyzed using SPSS (IBM Corp) software. To evaluate the differences in mean scores and identify sociodemographic factors associated with knowledge and attitudes toward the adoption of telemedicine, the data were analyzed using univariate and multivariable regression analyses, respectively. RESULTS: Overall, 1024 participants participated in the survey. The percentages of participants who attended a telemedicine service before, during, and after COVID-19 were 49.61% (508/1024), 61.91% (634/1024), and 50.1% (513/1024), respectively. The mean score for knowledge was 3.52 (SD 1.486; range 0-5), reflecting a high level of knowledge. The mean score for attitudes was 37.08 (SD 8.526; range 11-55), reflecting optimistic (positive) attitudes. Regarding barriers, the participants voiced some concerns regarding patient and physician resistance and attributed some cultural and technology-related limitations as potential hindrances to the full adoption of telemedicine services. The place of residence (rural vs nonrural) had a significant impact on the scores of knowledge, attitudes, and barriers, whereas gender had no significant impact. The multivariable regression analysis showed that several sociodemographic factors significantly correlated with knowledge and attitudes toward the adoption of telemedicine services. CONCLUSIONS: The participants showed good knowledge and positive attitudes toward telemedicine services. The perceived barriers were in line with the published literature. This research calls for the need to strengthen the positive attitudes and rectify the barriers, so that the utility of telemedicine services can be maximized in the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".