Post‐traumatic stress disorder and hiring: The role of social media disclosures on stigma and hiring assessments of veterans
Bibliographic record
Abstract
Abstract A significant percentage of veterans suffer from post‐traumatic stress disorder (PTSD). Veterans are often directed to social media platforms to seek support during their transition to civilian life. However, social media platforms are increasingly used to aid in hiring decisions, and these platforms may make veterans’ PTSD more discoverable during the hiring process. Based on social identity theory and identity management theory, the integrated suspicion model, and the stigma literature, we conducted four studies that examine veterans’ PTSD disclosures on social media and the consequences in the hiring process. Study 1 suggests that 16%–34% of veterans included cues related to PTSD status on social media. Study 2, based on 290 upper‐level business students, shows that veterans with PTSD were more stigmatized than veterans without PTSD, and stigmatization is associated with more suspicion and lower hiring‐related ratings (of expected task performance, expected organizational citizenship behaviors (OCB), expected counterproductive work behaviors (CWB), and intention to interview). Study 3, based on 431 working professionals with hiring experience, further supports relationships from Study 2. Study 4, based on 298 working professionals, identifies peril (i.e., perceptions regarding danger) as an additional mediator for the effects of PTSD on hiring‐related ratings. In sum, we identify and explore the identity management conundrum that social media disclosure poses for veterans with PTSD in the hiring process and discuss potential remedies and avenues for future research.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".