What is it about DID? A patient and clinician perspective
Bibliographic record
Abstract
SUMMARY The diagnosis of dissociative identity disorder (DID) remains a contentious area in mental health. Patients experiencing such difficulties are often harshly identified as suggestible neurotics and interested clinicians as fanatics. However, for the sufferer, DID is as real and has as much impact as any other psychiatric diagnosis. This commentary challenges psychiatry's dismissive and disbelieving attitude towards DID. The authors (a person with DID and a clinician) acknowledge the limited understanding of DID's aetiology and the paucity of associated neurological findings, but ask whether this is not the case for many other accepted psychiatric conditions. They call for UK psychiatric practice to move on from the debate and for the Royal College of Psychiatrists to take the lead, with inclusion of DID in core psychiatric training and guidelines on approaches to diagnosis and treatment.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 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".