Looking Through the Eyes of General Practitioners: The Role of Social Work in Primary Health Care
Bibliographic record
Abstract
Abstract In Flanders, Belgium, a primary healthcare reform is ongoing to strengthen the health system and work towards improving integrated care. At the core of this transformation stands a person-centred perspective that considers social factors, which increases the tendency for collaboration between health and welfare systems. Primary care physicians and social workers are urged to collaborate; however, the way general practitioners (GPs) define and utilise the role of social work remains unclear. This study explores the perceptions of GPs regarding the role of social work. Semi-structured interviews were conducted with twenty-three GPs, working under both fee-for-service and capitation financing systems, with varying years of experience and in different geographical areas. The findings reveal that physicians recognise the influence of social factors on their patients care needs, yet, struggle with addressing them. Due to limited experience and understanding of the role of social work, GPs primarily focus on its value in individual cases, whilst having less awareness of their role and potential at the neighbourhood, organisational or population level. This study identified different factors that either facilitate or hinder collaboration with social work. The implications for the social work profession and future joint efforts are discussed.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".