Examining the Construct Validity of Experimental Suicide Images Among Young Adults
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to validate and provide detailed norms on suicide images commonly used in experimental suicide research, and to examine whether appraisals of suicide images varied based on image features and prior suicidal thoughts and behaviors (STBs). METHOD: Young adults (N = 264) rated the extent to which images depicted someone "trying to kill themselves on purpose or who did kill themselves on purpose" (i.e., suicide ratings). Suicide ratings were examined descriptively and with bivariate and multivariable statistics. RESULTS: Suicide images demonstrated construct validity at image and aggregate levels. Further, suicide images looked more like suicide than pleasant, neutral, and interpersonal violence images, bs ≥ 5.653, ts ≥ 52.505, ps < 0.001. Among suicide images, suicide ratings were higher for images without compared to with gore, b = 0.269, t = 7.714, p < 0.001, and for images depicting high lethality methods (e.g., hanging, firearm) compared to the grand mean of all methods, bs ≥ 0.235, ts ≥ 3.316, ps < 0.001. Suicide ratings of suicide images were not associated with prior STBs. CONCLUSION: Using valid suicide images, like those tested in the current study, could improve behavioral methods designed to study processes related to STBs.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".