Assessing the quality of referral letters written by general practitioners: a cross-sectional study in rural Iran
Bibliographic record
Abstract
Establishing effective communication between general practitioners (GPs) and medical specialists is a key component of the referral system. Written communication between GPs and medical specialists is the most common communication tool. This study was conducted to evaluate quality (information content) of the referral letters written by GPs and addressed to gynecologists and cardiologists. We evaluated quality of the referral letters through a cross-sectional study in the villages of Sarab city, located in East Azerbaijan Province, Northwest Iran. The study was conducted during August and September 2015 in which a total of 400 referral letters were evaluated according to specific quality criteria. Cluster sampling was implemented and data were collected using an instrument designed by the Department of Family Medicine at the University of Manitoba, Canada. A specifically designed referral form was used to refer pregnant women to gynecologists. Referrals addressed to gynecologists showed better quality in comparison to cases referred to cardiologists. Legibility of referral letters was 73%. It is recommended that agreed-upon referral letters be designed cooperatively for different groups of diseases. Furthermore, primary health care providers should be trained to write proper referral letters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".