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The application of quality of life

2005· article· en· W1985156807 on OpenAlexaff
Roy I. Brown, Ivan Brown

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.010
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations103
Published2005
Admission routes1
Has abstractyes

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