Bibliographic record
Abstract
At the end of the period we had reached the following conclusions: (1) Doctors in rural general hospitals in Africa cannot avoid dealing with psychiatric patients. (2) Psychiatric problems of many kinds present and most can be managed and helped even in the unsophisticated setting of the mission hospital, with little extra effort on the part of the doctor. (3) An essential prerequisite is educating the whole hospital staff in the elements of psychiatric nursing and management. (4) It is essential to use a local person as linguistic and cultural inter preter. If this role can be combined with therapeutic skills it would be advantageous to all concerned. Thus medical auxiliaries can be trained to deal with most of the problems presenting. (5) There is much to be said for seeking the co-operation of a local traditional healer. (6) The co-operation of the patient's relatives should be encouraged throughout the therapeutic process. (7) Outpatient treatment may be difficult to organise, but should be the aim.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".