Socio-economic and Demographic Factors Influencing Interpersonal Communication Between Patients and Family Physicians: A Protocol for a Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Interpersonal communication is an essential aspect of patients’ relationships with their family physicians. It impacts patients’ experiences and the quality of care. Nevertheless, interpersonal communication can be affected by conscious and unconscious biases based on patients’ socio-economic and demographic characteristics. Evidence synthesis of this aspect of interpersonal communication in primary health care is limited. This systematic review will assess socio-economic and demographic factors influencing interpersonal communication between family physicians and patients living with one or more chronic conditions during clinical encounters. Methods We will perform a systematic review following the Joanna Briggs Institute Manual for Evidence Synthesis. The population of interest is adults living with at least one chronic condition. We will collect socioeconomic and demographic factors such as gender, sex, race or ethnicity, levels of literacy and/or health knowledge, level of education, and poverty or socioeconomic status, including employment or income level. Any published empirical study reporting aspects of interpersonal communication between patients and their family physicians will be considered. Three databases (Embase, MEDLINE, and Cochrane) will be assessed for eligible studies. Pairs of independent reviewers will select studies, extract data, and conduct bias assessment using MMAT-2018. We anticipate conducting descriptive and content analyses with narrative synthesis. Discussion Findings from this review may guide better communication between primary care physicians and their patients and increase awareness of potential health inequalities pathways in clinical practice. Registration number CRD42023411895 (PROSPERO platform).
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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 | 0.088 | 0.115 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.011 |
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".