Changes in suicide capability during short-term partial hospital treatment
Bibliographic record
Abstract
Background. Suicide capability (fearlessness about death [FAD], preparation, and perceived capability [PC]) is presumed to be static, or to increase with exposure to provocative events. However, tests of this assumption have primarily used non-clinical samples. We examined short-term changes in capability among patients in a partial hospital program. Methods. We enrolled 296 adult patients (186 [62.7%] female; Mage=36.13, SDage=14.75) who completed diagnostic interviews and questionnaires assessing demographics, capability, and suicide ideation (SI) at intake. Capability was re-assessed twice during treatment and again at discharge, and SI was re-assessed at discharge. We used latent growth modeling (LGM) to quantify changes in capability. Results. FAD, preparation, and PC decreased from intake to discharge (ps<.001, ds=0.24-0.63) and unconditional LGM models suggested that all facets changed significantly throughout treatment. FAD was positively associated with preparation (r=0.57) and PC (r=0.25) at intake (ps<.001), but changes in FAD did not predict changes in other facets. Greater SI at intake was concurrently associated with higher capability (βs=0.26-1.83, ps<.01) and predicted steeper declines in preparation (β=-0.23) and PC (β=-0.04), ps=.04. Finally, higher intake preparation predicted more severe SI at discharge (β=0.16, p<.001). Limitations. There was no control group and there was unequal spacing between in-treatment assessments among participants. Results for SI may have been impacted by floor effects. Conclusions. Results suggest that aspects of capability can change over a short time. Conceptually related facets of capability did not change together; thus, future investigations of the short-term dynamics of capability should not treat it as a unitary construct.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".