How do physicians perceive and respond to low income patients?
Bibliographic record
Abstract
Abstract How do physicians perceive and respond to low income patients? BACKGROUND: People with low socio-economic status have many health problems and relatively low access to medical and dental services. The dentists' attitudes, perceptions and ideas towards the low income patients have been shown to be significant determinants in accessing care. However, we have little information with respect to physicians. OBJECTIVES: The objective of this study was thus to determine the physicians' experiences with low income patients and in particular to identify the difficulties they encounter when dealing with deprived patients. We also wanted to identify the strategies and proposals suggested by physicians to improve health care for the deprived. METHOD: The study involved qualitative methodology using 7 open-ended interviews with physicians practicing in Quebec. The interviews were recorded on audiotapes and transcribed. The analyses consisted of debriefing the sessions, coding and interpreting the results. RESULTS: Three types of physicians were identified. The empathic physician looks beyond the physical problem of the deprived patient and tries to determine strategies to help on the social, psychosocial, financial and medical level. The blaming physician, on the contrary, tends to blame deprived patients for their laziness and abuse of the system. Its blaming attitude creates a communication and confidence barrier with the patients and complicates access to proper treatment. Finally, the indifferent physician treats the physical problems of all patients equally without concern about their social or socio-economic status. CONCLUSION: Like for dentists, the physicians' perc
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".