Brazilian Specialists’ Perspectives on the Patient Referral Process
Bibliographic record
Abstract
Since 1988, healthcare has been considered a citizen's right in Brazil. The Sistema Único de Saúde (SUS), has undergone development and expansion to ensure universal health coverage for the Brazilian public, the world's fifth largest population. The coordination of effective communications between primary care physicians, specialists and patients is a significant challenge, particularly the referral process. Our study objective was to understand the facilitators and barriers associated with referral process communications between primary care physicians and regional university hospital specialists in the State of Sao Paulo. This paper reports specialists' perspectives of the referral process. This was a phenomenological study that employed a qualitative research method with three components (description, reduction and comprehension). We conducted focus groups with 54 hospital residents from different specialties (surgery, medicine, obstetrics/gynecology, pediatrics) from July to October 2014. The main results showed lack of an adequate referral-return referral process resulting in treatment delays and inappropriate use of emergency services. Communications were impeded by lack of integrated, computerized booking and standardized referral-return referral processes; underlying lack of trust in primary care physicians; and patients' inappropriate use of healthcare services. Although computerized systems will facilitate communications between primary and specialty care, other strategies are needed to promote collaboration between services, and ensure appropriate utilization of them.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".