Digitally Mediated Therapeutic Relationships in Primary Care
Bibliographic record
Abstract
Context: Therapeutic relationships have been demonstrated as fundamental to primary care delivery. The rapid adoption of digital technologies since the onset of COVID-19 has led health care systems to consider or adopt a “digital-first” primary care delivery model. Questions remain regarding what impact this transformation will have on the relationships between primary care providers and patients. Objective: This study explores whether and how digital health technologies used in primary care create an environment that enables relationship-building between complex patients and their primary care providers. Study design and analysis: A rapid ethnographic approach including observations of virtual primary care visits and follow-up interviews with providers, patients and caregivers was used. Using social representation theory as a lens, observation and interview data were inductively analyzed (using thematic coding and visual mapping techniques) to uncover how patients and providers understand the role and value of digital technology as related to therapeutic relationships. Setting: Ethnographic data was collected across three primary care settings in the Greater Toronto Area and included two Family Health Teams and one Community Health Centre. Population studied: Participants included 10 primary care providers (5 physicians, 2 social workers, 1 nurse practitioner, 2 residents), 9 patients and 1 caregiver. 8 virtual care visits were observed. Results: Virtual care interactions were broadly influenced by patients’ and providers’ understanding of the nature and value of therapeutic relationships and technology. Personal characteristics (including technology comfort, roles, and identity), past experiences (with care delivery and technologies), and expectations (of what should occur in the clinical visit, or the desired outcome of that visit) informed how participants understood relationships and technologies; in turn, influencing whether virtual care was considered appropriate and effective by both patients and providers. Conclusion: Patients and providers come with expectations of virtual clinical interactions that are influenced by who they are and how they have experienced those interactions before. These findings have implications for how tools like virtual care platforms are developed (ideally through co-design) and implemented appropriately to attend to context, clinical situation, personal and professional identity, to enable shared meaning.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".