The Complexity of Patients' Health Communication Social Networks: A Broadening of Physician Communication
Bibliographic record
Abstract
Phenomenon: Patients have access to a wide variety of sources of information about their health in their day-to-day contexts. This can sometimes result in discordance between a physician's perception of a patient's health issue and a patient's perception of their health issue. Even after the physician has negotiated an understanding and treatment plan with a patient, subsequent interactions outside the physician–patient encounter may modify the patient's understanding of their health issue. A patient's reinterpretation of his or her health issue can then result in nonadherence of the treatment plan or even alternative treatment plans that the physician perceives as being unsatisfactory. Current models of physician–patient communication do not prepare physicians to manage this phenomenon. Approach: Using an ethnographic and a social network analysis research design, participants' patterns of social interaction around health information were investigated over a yearlong period (2012–2013) in a small rural community in Western Canada. Data included (a) individual interviews, (b) focus group interviews, and (c) field notes. Data were analyzed in a three-stage process: (a) item analysis, (b) pattern analysis, and (c) structural analysis. Findings: The findings highlight how physicians are only one nodal point in patients' broad, multilayered networks of communication. Interactions around health topics were not isolated events but rather occurred in various patterns of social interactions that were longitudinal and iterative. Meaning making around health topics was constructed, shared, elaborated, reconstructed, and interpreted in participants' social networks, as information was distributed through a complex temporal system of interpersonal ties. Insights: Issues concerning physician communication have been a long-standing conversation in the field of medical education. Many competency frameworks have attempted to encompass this core competency in their elaboration of the physician communicator. However, most representations and discussions in the field tend to depict physician communicators as experts who translate their knowledge to patients in a simplified way, in a single moment in time. This study suggests that educational initiatives in physician–patient communication would benefit from contextualizing physicians as part of patients' resource-rich, temporally extended, iterative process of meaning making. This alternative framing has the potential to support physicians' continuing engagement with patients as a meaningful and responsive node in patients' meaning-making networks.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".