Exploring social determinants of health in a Saudi Arabian primary health care setting: the need for a multidisciplinary approach
Bibliographic record
Abstract
BACKGROUND: Action on social determinants of health (SDH) in primary health care settings is constrained by practitioners, organizational, and contextual factors. The aim of this study is to identify barriers and enablers for addressing SDH in clinical settings in Saudi Arabia, taking into consideration the influence of local cultural and social norms, to improve care and support for marginalized and underserved patients. METHODS: We conducted a qualitative study involving individual in-depth interviews with a sample of 17 primary health care physicians purposefully selected based on the inclusion criteria, as well as a focus group with four social workers, all recruited from King Khalid University Hospital (KKUH) in Riyadh, Saudi Arabia. All interviews were audio-recorded, translated from Arabic to English, transcribed verbatim, and analyzed using thematic analysis following a deductive-inductive approach. RESULTS: According to study participants, financial burdens, challenges in familial dynamics, mental health issues and aging population difficulties were common social problems in Saudi primary health care. Action on SDH in primary care was hindered by 1) lack of physician knowledge or training; 2) organizational barriers including time constraints, patient referral/follow up; 3) patient cultural norms and 4) lack of awareness of physician's role in managing SDH. Enablers to more socially accountable care suggested by participants includes: 1) more education and training on addressing SDH in clinical care; 2) organizational innovations to streamline identification of SDH during patient encounters (e.g. case finding questionnaire completed in waiting room); 3) better interprofessional coordination and clarification of roles (e.g. when to refer to social work, what support is provided by physicians); 4) identifying opportunities for broader advocacy to improve living conditions for marginalized groups. CONCLUSION: Enabling more socially accountable care requires a multipronged approach including leadership from the Ministry of Health, hospital administrations and medical schools. In particular, there is a need for: 1) training physicians to help patients in navigating social challenges; 2) improving clinical/administrative interprofessional teams, 3) mobilizing local communities in addressing social challenges; and 4) advocating for intersectoral action to prevent health inequities before they become more complex issues presenting to clinical care.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".