Erratum to: Trends in Psychological/Psychiatric Injury and Law: Continuing Education, Practice Comments, Recommendations
Bibliographic record
Abstract
This literature review of the major topics in the field of psychological/psychiatric injury and law is aimed at developing practice in the area. The field is a fast-developing one, with over ten major topics that it needs to integrate. In particular, the present review focuses on current work on: law (evidence, tort); forensic psychology; assessment and testing; psychological injuries (posttraumatic stress disorder, chronic pain, traumatic brain injury, other); the APA DSM-5 draft (Diagnostic and statistical manual of mental disorders; American Psychiatric Association 2010); malingering; causality; multicultural considerations; disability; the American Medical Association (AMA) Guides to the evaluation of permanent impairment (Rondinelli et al. 2008); models; and treatment. At the end of each section of the article, practice comments introduce critical issues in applying the research to psychological work in the area. Whether undertaking tort evaluations, disability, and treatment plan assessments or treating individuals with psychological injuries, the professional needs state-of-the-art information in all the areas listed in order to remain scientifically informed, comprehensive, and impartial. The article concludes with recommendations for an integrated field in psychological/psychiatric injury and law, study in the field, research in its major areas, best practice policies, for example in assessment and treatment, and model building.
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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 | 0.010 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.041 | 0.028 |
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".