Trust and Uncertainty in the Implementation of a Pilot Remote Blood Pressure Monitoring Program in Primary Care: Qualitative Study of Patient and Health Care Professional Views
Bibliographic record
Abstract
BACKGROUND: Trust is of fundamental importance to the adoption of technologies in health care. The increasing use of telemedicine worldwide makes it important to consider user views and experiences. In particular, we ask how the mediation of a technological platform alters the trust relationship between patient and health care provider. OBJECTIVE: To date, few qualitative studies have focused on trust in the use of remote health care technologies. This study examined the perspectives of patients and clinical staff who participated in a remote blood pressure monitoring program, focusing on their experiences of trust and uncertainty in the use of technology and how this telehealth intervention may have affected the patient-provider relationship. METHODS: A secondary qualitative analysis using inductive thematic analysis was conducted on interview data from 13 patients and 8 staff members who participated in a remote blood pressure monitoring program to elicit themes related to trust. RESULTS: In total, 4 themes were elicited that showed increased trust (patients felt reassured, patients trusted the telehealth program, staff felt that the data were trustworthy, and a better patient-provider partnership based on the mutually trusted data), and 4 themes were elicited that reflected decreased trust (patients' distrust of technology, clinicians' concerns about the limitations of technologically mediated interactions, experiences of uncertainty, and institutional risk). CONCLUSIONS: Managing trust relationships plays an important role in the successful implementation of telemedicine. Ensuring that trust building is incorporated in the design of telehealth interventions can contribute to improved effectiveness and quality of care.
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.
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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".