Bibliographic record
Abstract
In this chapter, we discuss how Poplar Grove High School responded to suicide losses at the school. Crisis responses focused on suicide losses are often referred to as suicide “postvention,” and, when the losses are among youth, schools are a key site of response. Suicide postvention specifically encompasses the activities and organized interventions we undertake to limit suffering as much as possible, promote healing, and prevent additional suicides. Schools’ postvention strategies can greatly shape how the loss of a young person impacts the student body. Indeed, if there is any hope of encouraging the help-seeking we know is crucial yet frequently downplayed, schools must preventively establish and continuously revise evidence- and best-practices-based suicide postvention strategies. We begin by discussing why suicide postvention is so difficult for schools – in part because the science and funding is lacking. We by foregrounding youth’s experiences and words during postvention reveal how important it is to acknowledge the pain of suicide loss on a community. We then discuss the critical role of teachers, the structure of crisis counseling on the day of, and how inequalities in response can impact youth. We conclude by discussing reasons for hope for the future. For information on the methodology used in this study, please see https://annasmueller.files.wordpress.com/2019/04/2016_mueller_abrutyn_asr_under_pressure.pdf.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".