Bibliographic record
Abstract
Dr Edward Shorter' argues that part of the reason for the numerous fads that characterize psychiatry is the way our subject changes its paradigms, and wipes the slate clean each time it does so. The examples he gives are the asylum era, followed by the psychoanalytic era, ending in the era of psychopharmacology, neuroscience, and genetics. He points out that the shattered ruins of psychiatry's past the fads and fashions of today are built.'- p 558 He defines a fad as a bad idea with a short half-life, while a bad idea with a long half-life is a failed concept.In other medical specialties, a fad may be blown away by new evidence, but, in psychiatry, we tend to eliminate fads-when we do so by consensus-rather than by data. Dr Shorter writes that Psychiatry must be cautious about discarding, as fads, concepts that are in fact useful and worthy of pursuit. '*p 558 The Oedipus complex of psychoanalysis and the schizophrenogenic mother of social psychiatry may have been preposterous nonsense, but maternal attachment2-1 and the harm done by maternal deprivation4 and sexual and physical abuse5 were not. There are babies in the bathwater.Dr Joel Paris6 argues that fads are picked upfrom social groups, and shared concepts promote social bonding. In medicine, practitioners often follow what they hear from colleagues, from lectures, and from journals. Fads can spread rapidly when they fit needs for easy answers about diagnosis and treatment-and when supported by experts.6-p 561Both Dr Shorter' and Dr Paris6 give many examples of Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnoses that they considered faddish, and broaden their arguments to include referring to selective serotonin reuptake inhibitors (SSRIs) as antidepressants (ADs), and other fads brought about by Big Pharma in the naming of classes of drugs, and even to the endless proliferation of psychotherapies claiming to work by different methods. Thus the range and variety of fads is endless.One never knows how long what initially seems to be a fad may, in the long term, be a revolutionary advance; when it was first mooted, the World Wide Web was viewed with suspicion. How short does a half-life need to be? If calling SSRIs ADs is indeed a fad, it has been with us for over 30 years now. My main problem with fads is that many neither have short half-lives nor are they failed concepts: they are strongly held opinions without good supporting evidence. Prominent among these is the idea of comorbidity, based on the idea that the different disorders in the International Classification of Diseases or the DSM are independent disease entities. Alvan R Feinstein7 coined the idea of comorbidity to refer to those cases in which a distinct additional clinical entityp45
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".