(ID: 51) Barriers and facilitators to the provision of telemedicine in Nigeria: a systematic review
Bibliographic record
Abstract
Abstract Introduction Achieving Universal Health Coverage (UHC) remains a significant challenge in many developing countries, including Nigeria [1]. Telemedicine has emerged as a promising digital health intervention to bridge healthcare access gaps. However, its adoption and effectiveness vary due to diverse contextual barriers and facilitators [2]. Aim This systematic review aimed to identify the specific barriers and facilitators influencing telemedicine implementation in Nigeria. Methodology This review was pre-registered on PROSPERO (ID: CRD42024609405). Comprehensive searches were conducted across PubMed, Scopus, and CINAHL databases without time restrictions. Inclusion criteria encompassed peer-reviewed English-language studies reporting on barriers and/or facilitators related to telemedicine implementation, provision, or operation within the Nigerian context. Data extraction and thematic synthesis identified recurrent themes. No ethical approval was required, this being a review. Results Out of 384 identified studies, 31 met the inclusion criteria. The predominant barriers were technical and institutional. Technical barriers included unreliable power supply, poor internet connectivity, and a shortage of healthcare professionals with requisite technical expertise. Institutional barriers encompassed the absence of comprehensive regulatory frameworks and inadequate organizational policies supporting telemedicine. Conversely, facilitators were primarily human-resource-related, notably the provision of formal telemedicine training and education. Technical facilitators included the utilization of low-tech educational networks and improved internet accessibility. Discussion Telemedicine holds significant potential to enhance healthcare access in Nigeria. However, its successful implementation is impeded by technical and institutional challenges. Addressing these barriers through targeted interventions, such as investing in infrastructure, developing supportive policies, and enhancing workforce capacity through training, is crucial. This review, though subject to the usual limitations of study heterogeneity and scope, offers useful evidence that can guide telemedicine policy, practice, and future research.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".