Facilitators and barriers affecting the implementation of e-health for chronic respiratory diseases in remote settings: a qualitative evidence synthesis
Bibliographic record
Abstract
Abstract Background Chronic respiratory diseases are important causes of disability and mortality globally. Their incidence may be higher in remote locations where healthcare is limited and risk factors, such as smoking and indoor air pollution, are more prevalent. E-health could overcome some healthcare access obstacles in remote locations, but its utilisation has been limited. An improved understanding of barriers and facilitators to the implementation of e-health in remote locations could aid enhanced application of these approaches. Methods We performed a qualitative evidence synthesis to explore factors affecting the successful implementation of e-health interventions in remote locations for patients with chronic respiratory diseases. We searched PubMed, CINAHL, Embase, Web of Science and PsycINFO databases for qualitative and mixed-methods studies. Studies were assessed by two researchers, and 41 studies were included in the synthesis. Quality was assessed via the CASP-tool. Findings were coded with Atlas.ti software and categorised based on an adapted Digital Health Equity Framework. Results Nineteen themes were identified across five levels (individual, interpersonal, community, society and technology), with associated facilitators and barriers for implementation. An important facilitator of e-health was its role as a tool to overcome obstacles of distance and to increase access to care and patients’ self-efficacy. Potential barriers included the reduction of in-person interactions and an increased burden of work for healthcare providers. Good quality, usability, adaptability and efficacy of e-health interventions were important for implementation to be successful, as were adaptation to the local setting — including culture and language —and involvement of relevant stakeholders throughout the process. Conclusions Several factors affecting the implementation of e-health in remote and rural locations for patients with chronic respiratory disease were identified. Intervention objectives, target population, geographical location, local culture, and available resources should be carefully considered when designing an e-health intervention. These findings can be used to inform the successful design and implementation of future e-health interventions.
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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 | 0.102 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".