Bibliographic record
Abstract
While much has been written about how the media covers traumatic events, little is known about the impact of the media on trauma survivors. This, despite the fact that crime coverage has been a staple of daily news cycles for several decades. Likewise, little has been written about the training and methods of the journalists who cover these events, or the impact of this coverage on the journalists. Based on 71 qualitative surveys and interviews with homicide and traffic fatality survivors, and 22 qualitative surveys of journalists, this article serves to describe five main themes regarding survivor experiences: 1) Prior experience with the media; 2) First encounters with the media; 3) Negative impacts of the media; 4) Positive impacts of the media; and 5) Advice for various stakeholders. Additionally, this article will describe three main themes highlighted by the journalists: 1) Trauma-informed training and guidelines; 2) Comfort in contacting survivors; and 3) Personal impact of reporting on trauma. These findings illustrate a clear gap in services available to survivors, in particular in the immediate aftermath of traumatic events when media attention is often at its highest, as well as a lack of support for journalists covering these events.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".