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The application of quality of life

2005· article· en· W1985156807 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Intellectual Disability Research · 2005
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversity of TorontoUniversity of Victoria
Fundersnot available
KeywordsConceptualizationPopularityQuality of life (healthcare)PsychologyIntellectual disabilityQuality (philosophy)Work (physics)Engineering ethicsApplied psychologyGerontologyMedical educationMedicinePsychotherapistComputer sciencePsychiatrySocial psychologyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Despite its popularity, to date little systematic work has been done in the application of the quality of life (QOL) concept to persons with intellectual disability (ID) and its impact on individuals and families. This article addresses that need. METHOD: The article summarizes the four application strands suggested by the IASSID SIRG on Quality of Life regarding the application of the QOL concept and discusses critical aspects of each. RESULTS: Examples and guidelines regarding each strand are presented, along with the ongoing need to align conceptualization, application, and research efforts and integrate QOL principles into professional education and training programmes. CONCLUSIONS: The QOL concept is now challenging some of the more traditional views and approaches to ID. These challenges are resulting in modifications and adaptations in current services and supports, along with the need to evaluate the outcomes from the application of QOL principles to persons with ID.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.083
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.176
GPT teacher head0.478
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it