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Record W2108506156 · doi:10.3109/09638281003797414

Personal perception and personal factors: incorporating health-related quality of life into the International Classification of Functioning, Disability and Health

2010· article· en· W2108506156 on OpenAlexafffund
Jessica G. Huber, Jade Sillick, Elizabeth Skarakis‐Doyle

Bibliographic record

VenueDisability and Rehabilitation · 2010
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern University
FundersUniversity of Ottawa
KeywordsInternational Classification of Functioning, Disability and HealthBiopsychosocial modelPerceptionQuality of life (healthcare)PsychologyPerspective (graphical)RehabilitationPersonal developmentApplied psychologyPersonal lifeMedical model of disabilityClinical psychologyGerontologyMedicinePsychotherapistPsychiatryComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The International Classification of Functioning, Disability and Health (ICF), introduced by the World Health Organisation in 2001, offers a unique perspective from which to view the role of rehabilitation in one's lived experience of a health condition. However, the ICF does not capture the individual's perception of that experience that is key to understanding functioning, disability and quality of life (QOL) and more specifically health-related quality of life (HRQOL). The purpose is to explore expansion of the ICF framework to incorporate personal perception to offer a more complete expression of functioning and disability. METHOD: We examine the concepts of HRQOL and personal perception, as well as how they have been linked to the ICF in the literature. Through a review of the foundations of the biopsychosocial model, we propose an enhanced version of the ICF that integrates HRQOL within the framework by expanding the personal factors component. RESULTS: Through operationalising aspects of personal perception and situating them among the personal factors, we demonstrate how HRQOL may be integrated within the ICF framework. CONCLUSION: Using several case examples, we illustrate that if personal perception is housed within the personal factors component all other components may be influenced through mechanisms of the ICFs reciprocal interactions. In doing so, HRQOL becomes part of the experience of a health condition and functioning and disability are completely described.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.336
Teacher spread0.293 · 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 teacher head, 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

Citations47
Published2010
Admission routes2
Has abstractyes

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