Three-year trajectories of global perceived quality of life for youth with chronic health conditions
Bibliographic record
Abstract
PURPOSE: Objectives of this longitudinal study were to examine 3-year trajectories of global perceived quality of life (QOL) for youth with chronic health conditions, as obtained from youth and parent reports, and to identify personal and environmental factors associated with the trajectory groups for each perspective. METHODS: Youth with various chronic conditions aged 11-17 years and one of their parents were recruited from eight children's treatment centers. Latent class growth analysis was used to investigate perceived QOL trajectories (separately for youth and parent perspectives) over a 3-year period (four data collection time points spaced 12 months apart). Multinomial logistic regression was employed to identify factors associated with these trajectories. RESULTS: A total of 439 youth and one of their parents participated at baseline, and 302 (69 %) of those youth/parent dyads completed all four data collection time points. Two QOL trajectories were identified for the youth analysis: 'high and stable' (85.7 %) and 'moderate/low and stable' (14.3 %), while three trajectories were found for the parent analysis: 'high and stable' (35.7 %), 'moderate and stable' (46.6 %), and 'moderate/low and stable' (17.7 %). Relative to the 'high and stable' groups, youth with more reported pain/other physical symptoms, emotional symptoms, and home/community barriers were more likely to be in the 'moderate and stable' or 'moderate/low and stable' groups. Also, youth with higher reported self-determination, spirituality, family social support, family functioning, school productivity/engagement, and school belongingness/safety were less likely to be in the 'moderate and stable' or 'moderate/low and stable' groups, compared to the 'high and stable' groups. CONCLUSION: Findings suggest that youth with chronic conditions experience stable global perceived QOL across time, but that some individuals maintain stability at moderate to moderate/low levels which is related to ongoing personal and environmental influences. Potential benefits of universal strategies and programs to safeguard resilience for all youth and targeted interventions to optimize certain youths' global perceived QOL are indicated.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".