Applying the ICF framework to study changes in quality-of-life for youth with chronic conditions
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to describe how the ICF framework was applied as the foundation for a longitudinal study of changes in quality-of-life (QoL) for youth with chronic conditions. METHOD: This article will describe the study's aims, methods, measures and data analysis techniques. It will point out how the ICF framework was used--and expanded upon--to provide a model for studying the impact of factors on changes in QoL for youth with chronic conditions. Further, it will describe the instruments that were chosen to measure the components of the ICF framework and the data analysis techniques that will be used to examine the impact of factors on changes in youths' QoL. CONCLUSIONS: Qualitative and longitudinal designs for studying QoL based on the ICF framework can be useful for unraveling the complex ongoing inter-relationships among functioning, contextual factors and individuals' perceptions of their QoL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".