Review of patients’ perspectives of m-health adoption factors in the developing world. Development of a proposed conceptual framework
Bibliographic record
Abstract
Patient perceptions and experiences of mobile health (m-health) systems have been recognised as an important element to consider in the adoption of m-health based technologies. Though much research supports this, published studies that identify m-health use by patients appear to highlight these issues in an indirect rather than a holistic manner. Consequently, there is no encompassing framework that serves as a guide for effective implementation and maximum adoption of m-health from the perspective of patients in the developing world. This review identifies patient adoption issues specifically and uses these to develop a framework of patient adoption issues for m-health in the developing world. A structured literature search was conducted using PubMed and Scopus. For PubMed, a consolidated search string combined ‘MeSH’ terms and ‘All Fields’ terms for selected keywords. For Scopus, an equally consolidated search string was used. The searches were restricted to articles in English during the period January 1, 2000 to 31 December2019 and relevant to the developing world. Duplicate articles were removed. Titles and abstracts were screened by all authors for inclusion, and those studies that met the inclusion criteria were selected for full-text review. Review and data abstraction was performed by two authors. Fifty-four (54) articles reported factors that impact patient adoption. Initial review and data abstraction identified 22 categories that promote or impede m-health adoption by patients in the developing world. Continued iterative review reduced these to 7 primary categories, with 20 subcategories, which were used to design the proposed framework. The review showed: great inconsistency in the approach and tools used in published studies; multiple factors impact patient adoption of m-health in the developing world; the specific factors vary from setting to setting and by recency of findings. Successful adoption of m-health by patients in the developing world critically depends on addressing the factors identified in the proposed framework and assessing them prior to the implementation of m-health initiatives in any specific setting. The proposed framework will serve to increase the consistency of patient adoption studies and provide the foundation for greater success of future m-health implementations for patients in the developing world.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".