Sociodemographic factors and health conditions associated with the resilience of people with chronic diseases: a cross sectional study
Bibliographic record
Abstract
OBJECTIVE: to investigate the association between resilience and sociodemographic variables and the health of people with chronic kidney disease and / or type 2 diabetes mellitus. METHOD: a cross-sectional observational study performed with 603 people with chronic kidney disease and / or type 2 diabetes mellitus. A tool to collect socio-demographic and health data and the Resilience Scale developed by Connor and Davidson were applied. A descriptive and multivariate analysis was performed. RESULTS: the study participants had on average 61 years old (SD= 13.2), with a stable union (52.24%), religion (96.7%), retired (49.09%), with primary education (65%) and income up to three minimum wages. Participants with kidney disease showed less resilience than people with diabetes. CONCLUSION: the type of chronic illness, disease duration, body mass index and religious beliefs influenced the resilience of the study participants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".