Firesetting and Mental Health: Theory, Research and Practice
Bibliographic record
Abstract
Arson and other types of deliberate firesetting have major human and financial costs across the globe. People with mental disorder are disproportionately involved and mental health practitioners are frequently required to assess, treat and manage this troubling group. Half of all deliberate fire-related damage is caused by adults and this is the first book to take a comprehensive look at the issue from a mental health perspective. It brings research evidence, theory and practitioner advice into one accessible volume. Leading experts from the fields of psychiatry and psychology present current evidence on epidemiology, biological and psychological aetiology, and developmental aspects of deliberate firesetting. Contemporary overviews of best practice in relation to assessment and intervention, including in women and offenders with intellectual disability, are provided. Legal and fire safety experts present theoretical knowledge and practical advice on the role of mental health professionals in court and in fire prevention in clinical settings. The only available specialist text on firesetting behaviour in adults. Takes a broad mental health perspective and includes essential practical information about the law and fire prevention. Contributions from psychiatry, psychology, law and fire safety experts. International input by authors from the UK, Australia, Canada and the USA.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".