Maltreatment of children with disabilities: prevention strategies
Bibliographic record
Abstract
individual risk factor (disability).Instead, maltreatment was related to the existence of familial, social, health, cultural and economic risk factors.The greater the number of risk factors associated around a disabled child, the greater will be the possibility of maltreatment.3,4 Maltreatment of children with disabilities is part of the general problem of child maltreatment, which is still under-researched and little known as a whole; prevention, in particular, has received little attention.Preventing maltreatment against children with disability means to avoid physical pains, emotional sufferings, difficulties of integration and added consequences to those they already have due to their disability, besides to avoid big costs to the community.Investing from society in preventing maltreatment against children with disability may be more effective as for as the costs and give important and lasting profits.Wherever a prevention strategy is planned to be developed, it is essential to know the context of the problem in order to adapt the intervention to the target population, and concentrate the resources in the most vulnerable and risk groups.Maltreatment against children with disabilities is a multifactor problem, in which biological, psychological, social, economic and environmental factors take part, and for which there is not a simple or sole solution.On the contrary, is a problem that must be simultaneously tackled from several levels and in multiple sectors of society.Based on the perspective of the ecologic model of children maltreatment, that prevails nowadays, given that factors that can contribute to the maltreatment are diverse and interrelated, is necessary that different prevention strategies against maltreatment of children with disability are oriented in a multidisciplinary and multi sectorial way, and coordinately applied.5
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".