The Inappropriate Use of Risk-Benefit Analysis in the Risk Assessment of Experimental Trauma-Focused Research
Bibliographic record
Abstract
A large body of research has explored the impact of questioning participants about traumatic experiences. To determine the level of risk, these studies have relied, to various degrees, upon a risk-benefit calculus, whereby risks are weighed against the benefits that an individual can receive from participating. In the case of trauma-focused studies this approach is erroneous. The procedures involved in trauma-focused studies do not meet the criteria to be considered therapeutic, and the benefits associated with these procedures do not carry the moral weight to offset risk. Applying the risk-benefit calculus to non-therapeutic procedures inevitably leads to inaccurate risk assessments and ethically problematic claims, examples of which can be found throughout traumatic stress literature. This article outlines how the standard approach to risk assessment in trauma-focused studies is fallacious, and presents an established alternative model that researchers can use to accurately assess the risks of asking participants about their traumatic experiences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.661 | 0.778 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".