Barriers and facilitators of provision of telemedicine in Nigeria: A systematic review
Bibliographic record
Abstract
Healthcare access remains a challenge in developing countries and could be a drawback to the attainment of Objective 3 of the Sustainable Development Goals. Digital interventions such as telemedicine have been identified as an effective tool to improve healthcare access. However, evidence suggests that the impact of telemedicine is not uniform globally due to variances in barriers and facilitators. Thus, we conducted a systematic review to identify the barriers and facilitators of telemedicine in Nigeria. The systematic review was pre-registered on PROSPERO (Identification Number: CRD42024609405). Search was conducted on PubMed, Scopus, and the Cumulative Index of Nursing and Allied Health Literature databases. We included studies that reported on the estimates of barriers and facilitators of telemedicine in Nigeria as well as the factors associated with telemedicine implementation, provision, or operation in Nigeria. The outcome was the reportage of barriers and facilitators of telemedicine in Nigeria. A total of 384 studies were identified from the search. After the application of eligibility criteria and deletion of duplicates, 29 studies were included in the review. The most reported barriers were technical and institutional-related while the most reported facilitators were human-resource-related. Technical barriers frequently reported were power outages, poor internet connectivity, and paucity of health professionals with technical expertise while institutional barriers were lack of regulation and poor organizational policies. Formal telemedicine training and education were the most reported human resource facilitators while the use of low-tech educational networks and internet accessibility were the most reported technical facilitators. Findings from this review suggest that technical barriers are a challenge to adopting telemedicine in Nigeria. Evidence shows that education and training are critical in addressing these technical challenges. Thus, this review provides a background for interventions towards the effective implementation of telemedicine in Nigeria.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".