Quality of Life Indicators for Individuals With Intellectual Disabilities: Extending Current Practice
Bibliographic record
Abstract
Quality of life is a social construct that is measured by what are considered to be its most appropriate indicators. Quality of life measurement in intellectual disability reflects a variety of indicators, often grouped under life domains. Subjective and objective methods of measuring indicators each have strengths and drawbacks, but it is currently considered best to use both methods. Indicators of quality of life that are common to all people have been measured to date, although indicators that are unique to individuals are highly useful for enhancing individual development and for applying person-centered practice. Aggregate quality of life data from individuals may not always be the best source of information for evaluating policies and service practices. A case is made for supplementing quality of life frameworks or adopting other frameworks for these purposes, with the Capabilities Framework offered as an example. Further, an argument is made that a pragmatic approach might best be taken to policy and program evaluation, whereby the key criterion for using a conceptual framework and set of indicators is its usefulness in effecting positive change in people's lives.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".