Exploring the dynamics of physician‐patient relationships: Factors affecting patient satisfaction and complaints
Bibliographic record
Abstract
This review identifes the factors influencing the relationship between physicians and patients that can lead to patients' dissatisfaction and medical complaints. Utilizing a systemic approach 92 studies were retrieved which included quantitative, qualitative, and mixed method studies. Through a thematic analysis of the literature, we identified three interrelated main themes that can influence the relationship between physicians and patients, patients' satisfaction, and the decision to file a medico-legal complaint. The main themes include patient and physician characteristics; the interpersonal relationship between physicians and patients; and the health care system and policies, with relevant subthemes. These themes are demonstrated in a descriptive model. The review suggests areas of focus for physicians who may wish to increase their awareness around the potential sources of relational problems with their patients. Identifying these issues may assist in improvements in the therapeutic relationship with patients, can reduce their medico-legal risk, and enhance the quality of their clinical practice. The findings can also be utilized to support andragogical principles for medical learners. The article can serve as a structured framework to identify potential problems and gaps to design and test effective interventions to mitigate these potential relational problems between physician-patient.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".