The impact of eHealth on relationships and trust in primary care: a review of reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it