Content Validity of the Affective Disorder Subscale of the SIMS
Bibliographic record
Abstract
Background and Objective:The Structured Inventory of Malingered Symptomatology (SIMS) is used widely to "detect malingering" of medical symptoms, even though there is no convincing evidence that it does differentiate malingerers from patients with legitimate symptoms.This study focuses on the Affective Disorder (AF) subscale of the SIMS. Method:In Study 1, ten raters (3 psychologists and 7 psychiatrists), each with more than 35 years of clinical experience, evaluated whether the AF items have any capacity to differentiate malingerers from legitimate patients.Study 2 evaluated responses to AF items by 16 survivors of high impact car accidents (6 men and 10 women; mean age 36.6 years, SD=12.3).Study 3 compared responses of these 16 patients to SIMS responses of 30 instructed malingerers and also to 47 medical patients who sustained only relatively minor injuries in car accidents (data from a 2014 study led by Capilla Ramírez with González Ordi).Results: All ten raters agreed that none of the AF items would be endorsed only by malingerers: on the contrary, all AF items list only legitimate symptoms of depression.The most frequently endorsed items by our 16 postaccident patients were those dealing with lack of energy (100% of the patients) and sleep problems (93.8%).87.5% of these 16 patients who survived high impact car collisions would be falsely classified by the AF as "malingering an affective disorder."These 16 patients obtained significantly higher AF scores and higher total SIMS score than the 47 Spanish patients who sustained only relatively minor injuries in their car accidents (t-tests, p<.001).The 16 patients did not differ significantly in their AF and total SIMS scores from the instructed malingerers recruited in the Spanish study (p>.05). Discussion and Conclusions:The AF subscale of the SIMS contains no items with reasonable capacity to differentiate malingerers from legitimate patients.The SIMS is a fallacious test: its use on real patients is iatrogenic.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".