Trajectories of Self-Rated Health in People with Diabetes: Associations with Functioning in a Prospective Community Sample
Bibliographic record
Abstract
BACKGROUND: Self-rated health (SRH) is a single-item measure that is one of the most widely used measures of general health in population health research. Relatively little is known about changes and the trajectories of SRH in people with chronic medical conditions. The aims of the present study were to identify and describe longitudinal trajectories of self-rated health (SRH) status in people with diabetes. METHODS: A prospective community study was carried out between 2008 and 2011. SRH was assessed at baseline and yearly at follow-ups (n=1288). Analysis was carried out through trajectory modeling. The trajectory groups were subsequently compared at 4 years follow-up with respect to functioning. RESULTS: Four distinct trajectories of SRH were identified: 1) 72.2% of the participants were assigned to a persistently good SRH trajectory; 2) 10.1% were assigned to a persistently poor SRH trajectory; 3) mean SRH scores changed from good to poor for one group (7.3%); while 4) mean SRH scores changed from poor to medium/good for another group (10.4%). Those with a persistently poor perception of health status were at higher risk for poor functioning at 4 years follow-up than those whose SRH scores decreased from good to poor. CONCLUSIONS: SRH is an important predictor for poor functioning in diabetes, but the trajectory of SRH seems to be even more important. Health professionals should pay attention to not only SRH per se, but also changes in SRH over time.
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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 | 0.001 | 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".