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Record W4311781578 · doi:10.1007/s10862-022-10011-x

The Development and Validation of the Firesetting Questionnaire

2022· article· en· W4311781578 on OpenAlexaff
Theresa A. Gannon, Mark E. Olver, Emma Alleyne, Helen Butler, Victoria Lister, Caoilte Ó Ciardha, Katie Sambrooks, Nichola Tyler

Bibliographic record

VenueJournal of Psychopathology and Behavioral Assessment · 2022
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Kent
KeywordsPsychologyConfirmatory factor analysisClinical psychologyExploratory factor analysisTest (biology)PsychometricsStructural equation modeling

Abstract

fetched live from OpenAlex

Abstract This research developed and evaluated a measure to examine fire-specific constructs relevant to fire misuse. In the first study, using UK community members asked to disclose deliberate firesetting, we tested a large pool of theoretically informed questionnaire items. First, we found that 1 in 10 adults reported setting a deliberate fire that they had not been apprehended for. Then, exploratory and confirmatory factor analyses suggested an eight-factor measure with broader coverage of theoretically-informed risk factors, relative to previous measures, and acceptable test item validity and robust internal consistencies. In the second study, we tested the Firesetting Questionnaire with imprisoned men who held a record of firesetting and imprisoned and community comparisons. The findings illustrated psychometric robustness. Our results suggest that the Firesetting Questionnaire has the potential to be a useful clinical tool for highlighting fire-specific treatment needs and informing clinical formulation and associated risk management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.373
Teacher spread0.340 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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