Digitally mediated relationships: How social representation in technology influences the therapeutic relationship in primary care
Bibliographic record
Abstract
Relationships, built on trust, knowledge, regard, and loyalty, have been demonstrated to be fundamental to health care delivery. Strong relationships between patients and providers have been linked to more compassionate care delivery, and better patient experience and outcomes, and may be particularly important in primary care. The rapid adoption of digital technologies since the onset of COVID-19 has led health care systems to seriously consider a "digital-first" primary care delivery model. Questions remain regarding what impact this transformation will have on the therapeutic relationship. Using a rapid ethnographic approach this study explores how patient and provider understandings of therapeutic relationships and digital health technologies may influence relationship-building or maintenance between patients with complex care needs and their care providers. Three team-based primary care sites in Toronto, Ontario, Canada were included in the study. Across the three sites 9 patients with chronic health conditions, 1 caregiver, and 10 healthcare providers (including family physicians, family medicine residents, social workers, and nurse practitioners) participated. Interviews were conducted with all participants and 8 observations of virtual clinical encounters (phone and video visits) were conducted. Using social representation theory as a lens, analysis revealed that participants' constructions of therapeutic relationships and digital technologies were informed by their identities, experiences, and expectations. For participants to see technologies as enabling to the therapeutic relationship, there needed to be alignment between how participants viewed the role of technology in care and in their lives, and how they recognized (or constructed) a good therapeutic relationship. This exploratory work suggests the need to think about how both patients' and providers' views of technology may determine whether digital technologies can be leveraged to meet patient needs while maintaining, or building, strong therapeutic relationships.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".