An Investigation of Factors Predictive of Continued Self‐Injurious Behaviour in an Intellectual Disability Service
Bibliographic record
Abstract
Background Self‐injurious behaviour (SIB) is among the most serious problems faced by intellectual disability services. It is very difficult to treat and can become a chronic problem. Method Information on a number of variables was collected through a survey of service‐users identified as displaying SIBs. Clinical opinion and a literature review guided the selection of potential predictors of continued SIB. Univariate statistical analyses were used to investigate associations between continued SIB and each of the variables identified. Variables shown to have a significant association with continued SIB were subjected to a multivariate analysis to isolate those variables that still predicted continued SIB once the influence of the others had been accounted for. Results Two factors, self‐biting and verbal ability, were found to independently predict continued SIB. Conclusion The results have implications for intellectual disability services, in terms of the importance of multidisciplinary team working, training and guidelines for problem management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".