Characterizing Telehealth Barriers and Preferences to Promote Acceptable Implementation Strategies in Central Uganda: Multilevel Formative Evaluation
Bibliographic record
Abstract
Background: Telehealth approaches can address health care access barriers and improve care delivery in resource-limited settings around the globe. Yet, telehealth adoption in Africa has been limited, due in part to an insufficient understanding of effective strategies for implementation. Objective: This study aimed to conduct a multi-level formative evaluation identifying barriers and facilitators for implementing telehealth among health service providers and patients in Central Uganda. Methods: We collected surveys characterizing telehealth perceptions, barriers, and preferences from health care providers and patients seeking primary care in the Central Region of Uganda from January 2022 to July 2022. Survey development was informed by the technology acceptance model and evaluated predictors of technology acceptance (ie, perceived usefulness, ease of use, and attitudes). We used descriptive statistics to characterize telehealth perceptions and examined differences according to provider and patient characteristics using Student t tests. Results: Nearly 79% (n=48) of 61 providers surveyed had used telehealth, and perceptions were generally favorable. While 93.4% (n=57) reported that telehealth adds value to clinical practice, less than half (n=30, 49.2%) felt telehealth was more efficient than in-person visits. Provider-reported barriers to telehealth included technology challenges for the patient (34/132, 26%), low patient engagement (25/132, 19%), and lack of implementation support (24/132, 18%). Telehealth use was lower among the 91 surveyed patients, with only 19.8% (n=18) having used telehealth. Although 89% (n=81) of patients reported saving time with telehealth approaches, 33.3% (n=30) of patients reported that telehealth made them feel uncomfortable, and 43.8% (n=39) reported concerns about confidentiality. Over 72% (n=66) of patients who had used telehealth previously reported satisfaction with the telehealth services they received. Several differences in perceptions of telehealth according to patient's self-reported health status were observed. Conclusions: Perceptions of telehealth were generally favorable, although higher among providers than patients. Barriers impeding telehealth use include technology challenges and the lack of infrastructure and implementation support. Findings from this study can inform the implementation of acceptable telehealth approaches to address disparities propagated by health care access barriers in Sub-Saharan Africa.
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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 | 0.064 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".