Do professionals show a bias specific to treatment for people who have sexually offended in their interpretation of ambiguous evidence?
Bibliographic record
Abstract
The purpose of the current study was to examine whether professionals who work with people who have sexually offended are biased towards accepting ambiguous evidence as supportive of the effectiveness of treatment for these clients. In an online survey, professionals who work with people who have sexually offended (N = 58) were randomly assigned to receive a brief summary of ambiguous correlational evidence about either treatment for sexual offending or treatment for people who have cancer. Participants were then asked to select from causal and alternate interpretations of the evidence, whether they would recommend implementation of the treatment, and the proportion of funding they would allocate to implementation of the treatment. More than half of the participants incorrectly drew causal inferences and recommended implementing the treatment. However, there was limited evidence that this bias was specific to treatment for people who have sexually offended and there was no evidence that this bias was greater for participants who were more involved versus less involved in treatment for people who have sexually offended. Overall, our results most clearly suggest the operation of the pervasive, general critical thinking error of inferring causation from correlation rather than a self-serving or otherwise motivated bias.PRACTICE IMPACT STATEMENT The current study raises awareness of a common critical thinking error that can lead to the implementation of ineffective or even harmful practices and policies. We make recommendations to help reduce this error, which would facilitate more effective practice and policy.
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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 | 0.300 | 0.722 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".