Impact of severe intellectual disability on proxy instrumental assessment of quality of life
Bibliographic record
Abstract
Background: Proxy quality of life (QoL) evaluation has been reported to be influenced by many factors. The present study was designed to investigate the impact that the presence of severe intellectual disability (ID) may have on proxy attribution of QoL in the instrumental assessment.Methods: The “other person” form (proxy questionnaire) of the Italian adaptation of the Quality of Life Instrument Package (QoL-IP), the BASIQ [BAtteria di Strumenti per l’Indagine della Qualità di Vita] was administered to 20 first-line operators to assess their perceptions of the QoL of 92 subjects with severe ID and 34 volunteers without ID. The 54-item BASIQ measures three psychological domains (Being; Belonging; Becoming) and nine sub-domains, with each item also assessed across four dimensions (Importance; Satisfaction; Decision-making; and Opportunities).Results: Subjects with ID (as rated by proxies) had higher scores on BASIQ domains than those of non-ID subjects except for the sub-domain of Psychological Being. People with ID also received lower scores from proxies on the Decision-making dimension but higher scores on the Opportunities dimension. Differences between groups were statistically significant for most variables.Conclusions: Findings suggest that prejudicial attitudes towards the QoL of people with severe ID may be either absent in proxies or contained within the scope of the excercise. Previous research indicating that non-integrated QoL assessment may give paradoxical results was also supported.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".