Perspectives on effectiveness: what works in a juvenile fire awareness and intervention program?
Bibliographic record
Abstract
Deliberate lighting of fires by juveniles is both a public health concern and a community issue. This collaborative multiagency project aimed to establish best practice guidelines for child and youth firesetter programs in Australia. The study proceeded in two parts. Firstly, the practices and perceived effectiveness of the Victorian Juvenile Fire Awareness and Intervention Program (JFAIP) were investigated and contrasted with other Australian and overseas programs (US, Canada and NZ). Reviewing the literature, extensive interviewing, comparative analysis of approaches and site visits enabled the development of criteria associated with juvenile firesetter programs that were well designed, well implemented, and appeared to provide effective interventions. Secondly, pre and post fire-specific and psychosocial risk factors were investigated with a sample of 29 firesetter boys (7-13 years)referred to the JFAIP using the firesetting risk interview (FRI) and children’s firesetting interview (CFI). Children’s recidivism was also prospectively followed-up for 12 months. Pre and post findings on the FRI suggested that all JFAIP clients benefited from the intervention. From the parent’s perspective, lower fire-specific risk factors were reported after the intervention, but as expected psychosocial risks remained unchanged. From the child’s perspective on the CFI, some fire-specific risk variables had improved. Of the 29 children in the sample, nine participants were dentified as recidivists. Thus a third of the sample, although receiving an intervention, continued to light fires. Recidivist and nonrecidivist children were also compared on FRI and CFI and significant differences were found in both fire-specific and psychosocial risk factors. The study highlighted that high risk and low risk clients participate in fire safety education programs in Australia. Low risk clients benefited from a fire safety intervention emphasising education. Thus, fire safety education programs may be appropriate as a sole intervention with some firesetters under certain conditions. However, about a third of the JFAIP clients were recidivists and would benefit from additional interventions. It is recommended that juvenile firesetting programs follow best practice guidelines.
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 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 | 0.062 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".