Trajectories of health-related quality of life among people with a physical disability and/or chronic disease during and after rehabilitation: a longitudinal cohort study
Bibliographic record
Abstract
PURPOSE: To identify Health-related Quality of Life (HR-QoL) trajectories in a large heterogeneous cohort of people with a physical disability and/or chronic disease during and after rehabilitation and to determine which factors before discharge are associated with longitudinal trajectory membership. METHODS: A total of 1100 people with a physical disability and/or chronic disease were included from the longitudinal cohort study Rehabilitation, Sports and Active lifestyle. All participants participated in a physical activity promotion programme in Dutch rehabilitation care. HR-QoL was assessed using the RAND-12 Health Status Inventory questionnaire at baseline (T0: 3-6 weeks before discharge) and at 14 (T1), 33 (T2) and 52 (T3) weeks after discharge from rehabilitation. A data-driven approach using Latent Class Growth Mixture modelling was used to determine HR-QoL trajectories. Multiple binomial multivariable logistic regression analyses were used to determine person-, disease- and lifestyle-related factors associated with trajectory membership. RESULTS: Three HR-QoL trajectories were identified: moderate (N = 635), high (N = 429) and recovery (N = 36). Trajectory membership was associated with person-related factors (age and body mass index), disease-related factors (perceived fatigue, perceived pain and acceptance of the disease) and one lifestyle-related factor (alcohol consumption) before discharge from rehabilitation. CONCLUSIONS: Most of the people who participated in a physical activity promotion programme obtained a relatively stable but moderate HR-QoL. The identified HR-QoL trajectories among our heterogeneous cohort are disease-overarching. Our findings suggest that people in rehabilitation may benefit from person-centred advice on management of fatigue and pain (e.g. activity pacing) and the acceptance of the disability.
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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 | 0.001 | 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".