Physicians' social competence in the provision of care to persons living in poverty: research protocol
Bibliographic record
Abstract
BACKGROUND: The quality of the physician-patient therapeutic relationship is a key factor in the effectiveness of care. Unfortunately, physicians and people living in poverty inhabit very different social milieux, and this great social distance hinders the development of a therapeutic alliance. Social competence is a process based on knowledge, skills and attitudes that support effective interaction between the physician and patient despite the intervening social distance. It enables physicians to better understand their patients' living conditions and to adapt care to patients' needs and abilities. METHODS/DESIGN: This qualitative research is based on a comprehensive design using in-depth semi-structured interviews with 25 general practitioners working with low-income patients in Montreal's metropolitan area (Québec, Canada). Physicians will be recruited based on two criteria: they provide care to low-income patients with at least one chronic illness, and are identified by their peers as having expertise in providing care to a poor population. For this recruitment, we will draw upon contacts we have made in another research study (Loignon et al., 2009) involving clinics located in poor neighbourhoods. That study will include in-clinic observations and interviews with physicians, both of which will help us identify physicians who have developed skills for treating low-income patients. We will also use the snowball sampling technique, asking participants to refer us to other physicians who meet our inclusion criteria. The semi-structured interviews, of 60 to 90 minutes each, will be recorded and transcribed. Our techniques for ensuring internal validity will include data analysis of transcribed interviews, indexation and reduction of data with software qualitative analysis, and development and validation of interpretations. DISCUSSION: This research project will allow us to identify the dimensions of the social competence process that helps physicians establish therapeutic relationships with low-income patients living with chronic illness. This study will also offer concrete recommendations for improving health interventions among low-income patients and for helping them to better manage their chronic illnesses. Ultimately, our aim is to strengthen the capacity of the health care system and of professionals to provide care that is adapted to the social conditions of people living in poverty.
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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 | 0.086 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".