Physician experiences and barriers to addressing the social determinants of health in the Eastern Mediterranean Region: a qualitative research study
Bibliographic record
Abstract
BACKGROUND: While it is increasingly recognized that social determinants influence the health of patients and populations, little is known about how doctors in the Eastern Mediterranean Region can help their patients with these issues. Our study aimed to identify common social challenges faced by patients in Eastern Mediterranean countries, to assess what doctors are already doing to address these challenges, and to identify barriers and facilitators for addressing the social causes of poor health in Eastern Mediterranean countries with shedding some light on how does this compare to a developed country like Canada. METHODS: We conducted a qualitative research study employing qualitative descriptive methodology. A purposeful sample as well as snowballing technique were used to recruit 18 physicians who were trained in Eastern Mediterranean countries but have since moved to Canada. Recruitment continued until data saturation was reached. A content analysis was carried out after transcribing the interviews. RESULTS: The main social challenges identified in clinical care in Eastern Mediterranean Regions include poverty, illiteracy, domestic violence, and food insecurity. Doctors attempted to help their patients by providing free medical services and free medications, establishing a donation box, and referring to social workers and support services, where available. Cultural constraints, lack of time, and unavailability of referral resources were often cited as important barriers. Our participants stated that Canada is generally better in dealing with the social challenges than their countries of origin. CONCLUSIONS: Most study participants expressed their willingness to help patients in dealing with social challenges, and shared their experiences of tackling such issues, though there were also important barriers reported that would need to be overcome. Participants suggested that better addressing social challenges in clinical care would require educating both health care providers and patients about the importance of discussing the patient's social environment as part of the health care encounter, as well as advocating for broader policy approaches by governments to address the underlying social problems.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".