Accuracy and predictive value of incarcerated adults' accounts of their self-harm histories: findings froman Australian prospective data linkage study
Bibliographic record
Abstract
BACKGROUND: Self-harm is prevalent in prison populations and is a well-established risk factor for suicide. Researchers typically rely on self-report to measure self-harm, yet the accuracy and predictive value of self-report in prison populations is unclear. Using a large, representative sample of incarcerated men and women, we aimed to examine the level of agreement between self-reported self-harm history and historical medical records, and investigate the association between self-harm history and medically verified self-harm after release from prison. METHODS: During confidential interviews with 1315 adults conducted within 6 weeks of expected release from 1 of 7 prisons in Queensland, Australia, participants were asked about the occurrence of lifetime self-harm. Responses were compared with prison medical records and linked both retrospectively and prospectively with ambulance, emergency department and hospital records to identify instances of medically verified self-harm. Follow-up interviews roughly 1, 3 and 6 months after release covered the same domains assessed in the baseline interview as well as self-reported criminal activity and contact with health care, social and criminal justice services since release. RESULTS: Agreement between self-reported and medically verified history of self-harm was poor, with 64 (37.6%) of 170 participants with a history of medically verified self-harm disclosing a history of self-harm at baseline. Participants with a medically verified history of self-harm were more likely than other participants to self-harm during the follow-up period. Compared to the unconfirmed-negative group, the true-positive (adjusted hazard ratio [HR] 6.2 [95% confidence interval (CI) 3.3-10.4]), false-negative (adjusted HR 4.0 [95% CI 2.2-6.7]) and unconfirmed-positive (adjusted HR 2.2 [95% CI 1.2-3.9]) groups were at increased risk for self-harm after release from prison. INTERPRETATION: Self-reported history of self-harm should not be considered a sensitive indicator of prior self-harm or of future self-harm risk in incarcerated adults. To identify those who should be targeted for preventive strategies, triangulation of data from multiple verifiable sources should be performed whenever possible.
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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 | 0.042 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".