Socioeconomic and demographic factors influencing interpersonal communication between patients with chronic conditions and family physicians: A systematic review
Bibliographic record
Abstract
This systematic review assessed the socioeconomic and demographic factors influencing interpersonal communication between family physicians and patients with chronic diseases. We searched three databases (Embase, MEDLINE, and Cochrane) for published empirical studies reporting interpersonal communication between adults with chronic conditions and their family physicians. Gender, sex, race or ethnicity, low levels of literacy and/or health knowledge, and lower level of education or income were the factors of interest. Pairs of independent reviewers selected studies, extracted data, and appraised quality of the studies using MMAT-2018. We conducted descriptive and content analyses with a narrative synthesis. From 7579 identified deduped studies, we included 12 with a total of 22266 participants. Suboptimal interpersonal communication in several domains was more incident amongst ethnic minorities (p < 0.01) and individuals with lower language proficiency (p < 0.05). Studies used sex and gender interchangeably. The classifications of racial and ethnic origin, income, and education levels were inconsistent. Our findings suggest that socioeconomic and demographic factors can affect deleteriously in-encounter interpersonal communication. Practice Implications: This review might help guide a communication curriculum for medical students and increase awareness of potential health inequalities pathways in clinical practice. Registration number: CRD42023411895 (PROSPERO platform). • Interpersonal communication impacts patients' experiences and the quality of healthcare. • Gender and race (i.e., ethnic or socio-cultural background) are the most studied factors of interpersonal communication. • There is a lack of studies concerning uncertainty management, self-management encouragement, and responding to emotions. • Ethnicity and decreased language proficiency increase the risk of suboptimal interpersonal communication between patients with chronic conditions and family physicians.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".