Personal perception and personal factors: incorporating health-related quality of life into the International Classification of Functioning, Disability and Health
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".