The Impact of COVID-19 on the Prevalence and Perception of Telehealth Use in the Middle East and North Africa Region: Survey Study
Bibliographic record
Abstract
BACKGROUND: Due to the COVID-19 pandemic, telehealth has become a safer way to access health care. The telehealth industry has rapidly expanded over the last decade as a modality to provide patient-centered care. However, the prevalence of its use and patient acceptability remains unclear in the Middle East and North Africa (MENA) region. OBJECTIVE: The primary aim was to assess the prevalence of telehealth use before and during the pandemic by using social media (Instagram) as an online platform for survey administration across different countries simultaneously. Our secondary aim was to assess the perceptions regarding telehealth among those using it. METHODS: An Instagram account that reaches 130,000 subjects daily was used to administer a questionnaire that assessed the current prevalence of telehealth use and public attitudes and acceptability toward this modality of health care delivery during the COVID-19 pandemic. RESULTS: A total of 1524 respondents participated in the survey (n=1356, 89% female; median age 31 years), of whom 97.6% (n=1487) lived in the Gulf Cooperation Council (GCC) region. Prior to COVID-19, 1350 (88.6%) had no exposure to telehealth. Following the COVID-19 pandemic, telehealth use increased by 251% to a total of 611 users (40% of all users). About 89% (571/640) of telehealth users used virtual visits for specialist visits. Of the 642 participants who reported using telehealth, 236 (36.8%) reported their willingness to continue using telehealth, 241 (37.5%) were unsure, and 164 (25.5%) did not wish to continue to use telehealth after the COVID-19 pandemic. An inverse trend, although not statistically significant, was seen between willingness to continue telehealth use and the number of medical comorbidities (odds ratio [OR] 0.81, 95% CI 0.64-1.03; P=.09). Compared to the respondents who chose only messaging as the modality they used for telehealth, respondents who chose both messaging and phone calls were significantly less likely to recommend telehealth (OR 0.42, 95% CI 0.22-0.80; P=.009). Overall, there was general satisfaction with telehealth, and respondents reported that telehealth consultations made them feel safer and saved both time and money. CONCLUSIONS: Telehealth use increased dramatically after the COVID-19 pandemic, and telehealth was found to be acceptable among some young adult groups on Instagram. However, further innovation is warranted to increase acceptability and willingness to continue telehealth use for the delivery of health care.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".