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Record W2250124424

Firesetting and Mental Health: Theory, Research and Practice

2012· book· en· W2250124424 on OpenAlexaboutno aff
Geoffrey L. Dickens, Philip Sugarman, Theresa A. Gannon

Bibliographic record

Venuenot available
Typebook
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsArsonMental healthGlobePsychologyMental health lawPerspective (graphical)PsychiatryIntervention (counseling)Psychological interventionCriminologyMedicineApplied psychology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.691
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.252
GPT teacher head0.575
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations31
Published2012
Admission routes1
Has abstractyes

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