Quality of life for people with intellectual disabilities
Bibliographic record
Abstract
PURPOSE OF REVIEW: As the quality of life for the mental health of people with intellectual disabilities has been identified as a comprehensive indicator of intervention efficacy, scientific interest around it is shifting from theoretical issues to the ways of measurement. Nevertheless, the daily assessment still awaits the addressing of the questions of what the essence of quality of life is, how it is really or effectively measurable, by whom and for what purpose it is done. RECENT FINDINGS: The point well agreed upon is that the measurement should be based on both qualitative and quantitative variables from both subjective and objective positions. It should also be conducted through a comprehensive system that includes auto and hetero evaluations (if possible both by proxies and other external persons). Current instruments of assessment are too different from each other and refer to different levels of evaluation. This results in inappropriate applications to the assessment and care procedures. SUMMARY: A need for more methodologically rigorous studies exists, which is tracked in terms of applicability to daily practice and content effectiveness. At both health policy and front-line staff levels, assessment should aim at mobilizing and revaluing resources that can help a person to embark on or to continue a life-span curriculum of life skills.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".