Feasibility of a Cost-Effectiveness Analysis Examining Interventions for Abused Persons with Intellectual Disabilities
Bibliographic record
Abstract
Japan implemented new legislation to prevent the abuse of persons with disabilities on Oct 1, 2012. Many specialists from various domains participated in the development of interventions to prevent such abuse. Here, we conducted a pilot analysis to examine the cost of such interventions and to explore differences in resources. In particular, we compared resources for the assistance of victims with intellectual disabilities with those for the assistance of victims with other disabilities. We requested the enrollment of the anonymous case records of 16 local governments. Thirteen municipal/certified centres reported 41 cases, including 42 victims. Of them, 27 victims had intellectual disabilities. We calculated both the time and human/social resources consumed per case until the resolution of the case. Although the median length of time from the start of the intervention until the solution of the claimed crisis seemed longer in cases abused by their families, an analysis of 22 familial cases did not reveal a significant relationship between the type of disability and the resource. Although the existence of intellectual disabilities did not seem to impact the resource, our method of analysis worked well. The accumulation of more cases is warranted.
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 | 0.048 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".