The impact of eHealth on relationships and trust in primary care: a review of reviews
Bibliographic record
Abstract
Abstract Background Given the increasing integration of digital health technologies in team-based primary care, this review aimed at understanding the impact of eHealth on patient-provider and provider-provider relationships. Methods A review of reviews was conducted on three databases to identify papers published in English from 2008 onwards. The impact of different types of eHealth on relationships and trust and the factors influencing the impact were thematically analyzed. Results A total of 79 reviews were included. Patient-provider relationships were discussed more frequently as compared to provider-provider relationships. Communication systems like telemedicine were the most discussed type of technology. eHealth was found to have both positive and negative impacts on relationships and/or trust. This impact was influenced by a range of patient-related, provider-related, technology-related, and organizational factors, such as patient sociodemographics, provider communication skills, technology design, and organizational technology implementation, respectively. Conclusions Recommendations are provided for effective and equitable technology selection, application, and training to optimize the impact of eHealth on relationships and trust. The review findings can inform providers’ and policymakers’ decision-making around the use of eHealth in primary care delivery to facilitate relationship-building.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".