Doling out too little for priority sake: an audit of referral letters to a tertiary psychiatric unit in Nigeria.
Bibliographic record
Abstract
Background: A referral process seeks the assistance of a better or differently resourced facility at the same or higher level to assist in, or to take over the management of the client's case. The referrals received at the psychiatric unit of our tertiary health care facility from across the clinical specialties vary in both quality and content. Objective: This study aimed to assess quality of the content and highlight the important elements of 261 referral letters received at the Department of Psychiatry of the Ekiti State University Teaching Hospital (EKSUTH), southwest Nigeria. Method: In the assessment of the letters, a checklist adapted from the University of Manitoba was used carefully to evaluate the quality of each referral letter. Result: More than half, 147 (56.3%), of the letters were received from the adult emergency unit. About a third (31.0%) of the letters had incomplete biodata of the patients; and one out four of the letters did not indicate the reason for the referral. Majority of the referral letters did not give relevant information about patients regarding psychosocial history, clinical findings. About 60% of letters that referred known psychiatric patients gave information on neither previous episodes of psychiatric illness, nor relevant clinical findings. More than a quarter (27.2%) of the referral letters under analysis did not express statement of what was expected, by the referring clinicians, for the patients. Conclusion: Earnest efforts should be made to include the art of medical communication in both undergraduate and postgraduate medical education curriculum.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".