Healthcare Workers’ Perspectives of mHealth Adoption Factors in the Developing World: Scoping Review
Bibliographic record
Abstract
BACKGROUND: mHealth applications provide health practitioners with platforms that enable disease management, facilitate drug adherence, facilitate drug adherence, speed up diagnosis, monitor outbreaks, take and transfer medical images, and provide advice. Many developing economies are investing more in mobile telecommunication infrastructure than in road transport and electric power generation. Despite this, mHealth has not seen widespread adoption by healthcare workers in the developing world. This study reports a scoping review of factors that impact the adoption of mHealth by healthcare workers in the developing world, and based on these findings, a framework is developed for enhancing mHealth adoption by healthcare workers in the developing world. METHODS: A structured literature search was performed using PubMed and Scopus, supplemented by hand searching. The searches were restricted to articles in English during the period January 2009 to December 2019 and relevant to the developing world that addressed: mobile phone use by healthcare workers and identified factors impacting the adoption of mHealth implementations. All authors reviewed selected papers, with final inclusion by consensus. Data abstraction was performed by all authors. The results were used to develop the conceptual framework using inductive iterative content analysis. RESULTS AND DISCUSSION: Of 919 articles, 181 met the inclusion criteria and, following a review of full papers, 85 reported factors that impact (promote or impede) healthcare worker adoption of mHealth applications. These factors were categorised into 18 themes and, after continued iterative review and discussion were reduced to 7 primary categories (engagement/funding, infrastructure, training/technical support, healthcare workers' mobile-cost/ownership, system utility, motivation/staffing, patients' mobile-cost/ownership), with 17 sub-categories. These were used to design the proposed framework. CONCLUSIONS: Successful adoption of mHealth by healthcare workers in the developing world will depend on addressing the factors identified in the proposed framework. They must be assessed in each specific setting prior to mHealth implementation. Application of the proposed framework will help shape future policy and practice of mHealth implementation in the developing world and increase adoption by health workers.
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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 | 0.013 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".