The DSM-5 controversies: How should psychologists respond?
Bibliographic record
Abstract
The American Psychiatric Association (APA) published DSM-5 in May 2013. The revision process was fraught with controversy. In the first section of this article, we briefly summarise the controversies related to the actions of the APA and the Task Force responsible for the revision process. These include allegations of secrecy, accusations of conflicts of interest, apprehension over a promised paradigm shift, concerns about the definition of mental disorder, charges of medicalizing normality, and claims of poor methodology. In the second section, we briefly summarise the controversies related to some of the revisions to the DSM-5 disorders and diagnostic criteria. In the third section, we argue that DSM-5 development was unnecessarily contentious for reasons that could have been foreseen and prevented. Because incremental updates to the DSM-5 are anticipated in the near future (American Psychiatric Association, 2010, APA modifies DSM naming convention to reflect publication changes, Washington, DC: Author), we propose that psychologists external to the revision process should use their unique expertise to assist in resolving the controversies that have beset the DSM-5 and thereby facilitate a less contentious development of the next iteration of the DSM.
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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 | 0.131 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.036 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 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".