When Social Inequalities Produce “Difficult Patients”: A Qualitative Exploration of Physicians’ Views
Bibliographic record
Abstract
“Difficult patients” are most often characterized by their personality, behavior, or pathology. Little research has been done to understand how some patients, because of their disadvantaged social position, are perceived as “difficult” by their physicians. Our qualitative, exploratory study was conducted to understand how social inequalities contribute to this perception of a “difficult patient.” It was based on 12 semi-structured interviews with physicians, in Montréal, Canada. Participants identified three main challenging factors: a perception of excessive time required to manage these patients; a feeling of ineffectiveness, despite this additional time spent; and the pressure to “do something” about needs they perceive as serious, despite this feeling of ineffectiveness. To adjust their practice to the specific circumstances of these disadvantaged patients, they feel it is important to establish good relationships with them, to set realistic objectives, and to increase interprofessional interactions. We discuss these findings in relation to three issues that contribute to this sense of difficulty: the social distance between physicians and patients, the social determinants affecting patients’ health, and certain aspects of the health care system that impede the above-mentioned adjustments in medical practice. By exploring, social inequalities in health care access not through the experience of patients, but rather through the perspectives of the physicians who feel unable to protect them, this analysis highlights that the scope of action required to address such inequalities far exceeds physicians’ practice.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".