Unpacking the Meaning of Closeness, Reconsidering the Concept of Impact in Suicide Exposure, and Expanding Beyond Bereavement: “Just, I Hope You Don’t Forget About Us”
Bibliographic record
Abstract
Suicide exposure research has relied on samples of treatment-seeking kin, resulting in an attachment-based model centering bereavement as the most significant form of impact and obscuring other forms of significant and life-altering impact. From a community-based sample ( N = 3010) exposed to suicide, we examine a subset ( n = 104) with perceived high impact from the death yet low reported closeness to the person who died and analyze qualitative comments ( n = 50). On average and out of 5.00, participants rated closeness as 1.56 but impact of death as 4.51. We illustrate dimensions of low closeness and identify themes on the meaning of impact: impact through society and systemic circumstances, impact through history and repeated exposure, impact through other people, impact as a motivator for reflection or change, and impact through shared resonance. Participants reported impact of death as significant or devastating, yet none of their comments reflected experiences typical of bereavement.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".