Possibilities and Expectations for mHealth in the Pacific Islands: Insights From Key Informants
Bibliographic record
Abstract
BACKGROUND: The increase in mobile phone use across the globe is creating mounting interest for its application in addressing health system constraints. Although still limited, there is growing evidence of success in using mobile phones for health (mHealth) in low- and middle- income countries. The promise of mHealth to address key health system issues presents a huge potential for the Pacific Island countries where mobile use has radically increased. Current projections indicate an improved information and communications technology (ICT) environment to support greater access to mobile and digital devices in the Pacific region. OBJECTIVE: The objective of the study was to explore key stakeholder perspectives on the potential for mHealth in the Pacific region. METHODS: A series of in-depth interviews were conducted either face-to-face, via Skype or by email, with a series of key informants from the Pacific Rim region. Interviews were audio-recorded and later transcribed for detailed thematic analysis. RESULTS: We found widespread support for the potential to use mobile phones as a mechanism to facilitate improved health service delivery in the region. Essential elements for the successful development and implementation of mHealth were identified by these stakeholders. These included: developing an understanding of the local context and the problems that may be usefully addressed by the addition of mHealth to existing strategies and services; consideration of local infrastructure, capability, policy, mobile literacy and engagement; learning from others, particularly other low- and middle-income countries (LMICs); the importance of building supportive environments and of evaluation to provide evidence of impact and total cost. CONCLUSIONS: The rapid growth of mobile phone use in the region presents a unique juxtaposition of opportunity and promise. Though the region lags behind other LMICs in the adoption of mHealth technologies, this offers the convenience of learning from past mHealth interventions and applying these learnings to achieve scale, sustainability and success. This study deepens the understanding of the potential of mHealth for the region, and offers a baseline from which discussions can be made to examine the limitations, barriers and complexities inherent in mHealth applications.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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".