Exploring Health Professionals’ Knowledge and Perceptions of Telehealth Implementation in Multisite Public Hospitals of East Wollega Zone, Oromia, West Ethiopia: Cross-Sectional Study
Bibliographic record
Abstract
Background: A significant gap exists among health professionals regarding telehealth due to limited knowledge and varying perceptions. This disparity is particularly pronounced in low-income regions, where numerous barriers hinder its implementation. Objective: This study aimed to assess the knowledge and perception of telehealth among health professionals in public hospitals in East Wollega, Oromia Region, Ethiopia. Methods: A study was conducted with 397 health professionals in public health facilities in East Wollega Zone, selected through systematic random sampling. Data were collected via self-administered questionnaires from March 2024 to June 2024 and entered into EpiData (version 3.1; EpiData Association) and analyzed using SPSS (version 24; IBM Corp). Descriptive statistics, including percentages and frequency distributions, were used. The Hosmer and Lemeshow test assessed variable homogeneity. Significant variables (P<.05) in bivariable logistic regression analysis were included in multivariable logistic analysis and reported as adjusted odds ratios (AOR) with 95% CI. Results: Findings indicated high levels of knowledge and perception among participants. Factors significantly associated with knowledge included educational status (AOR 4.22, 95% CI 3.45-13.12; P=.001), income between Ethiopian Birr 7001-9000 (a currency exchange rate of Ethiopian Birr 52=US $1 during the study period; AOR 3.255, 95% CI 1.790-8.878; P=.001), access to computers (AOR 2.414, 95% CI 1.046-14.764; P=.001), consultation sessions (AOR 2.389, 95% CI 1.961-10.158; P=.001), and smartphone use (AOR 3.027, 95% CI 2.797-14.729; P=.001). For perception, significant associations included income over 9000 Birr (AOR 2.675, 95% CI 2.271-19.277; P=.001), improving quality of life (AOR 1.786, 95% CI 1.575-22.587; P=.009), increased accessibility (AOR 1.244, 95% CI 1.061-11.333; P=.04), consultation sessions (AOR 4.777, 95% CI 2.318-15.062; P=.001), and smartphone use (AOR 3.836, 95% CI 2.900-13.573; P=.001). Conclusions: Approximately two-thirds of participants demonstrated good knowledge and perception of telehealth. It is highly recommended to create awareness through educational programs for individuals with lower educational backgrounds and to improve access to technological devices.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".