Trigger warnings: A quantitative study on the stigmatization of individuals with a mental illness and university students’ help-seeking intentions
Bibliographic record
Abstract
Requests for trigger warnings before distressing content in the university classroom have increased, especially to accommodate individuals with a history of trauma. However, no empirical evidence has been collected on the stigmatizing nature of trigger warnings. The trigger warning debate has received mainstream media attention and draws dichotomous lines between those who believe in the protective nature of trigger warnings, and those who believe they are coddling to students. The trigger warning literature is limited, however, and focuses mainly on how trigger warnings affect anticipated or experienced anxiety, emotional regulation, and post-traumatic stress. To date, the literature fails to investigate how trigger warnings influence stigma towards those who may benefit from them most, namely, individuals with a mental illness, and whether trigger warnings influence help-seeking intentions. In this study, participants were psychology students recruited from the University of Guelph. Design: 2 x 2 repeated measures split-plot design with two phases: 1) participants filled out an online survey to provide a baseline for phase two, and 2) participants were randomized into either a trigger warning or control condition and subsequently filled out the same online survey. Analysis: 2 x 2 analysis of variance for each dependent variable (stigma, help-seeking intentions). Results: In this sample, trigger warnings did not have an effect on students’ stigmatization toward individuals with a mental illness or their help-seeking intentions. This paper is an abridged version of one that has been uploaded to the Open Science Foundation website and can be found under this project: (osf.io).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".