Measurement of psycho-emotional constructs and self-management in health of patients with Rheumatics diseases
Bibliographic record
Abstract
Objective: This study aimed to measure the scores of psychoemotional and self-management constructs in patients with rheumatic diseases (RD), to compare these scores according to the RD type and to verify the influence of these measures on self-management in health.Methods: Cross-sectional study, carried out in an ambulatory of a public hospital of Brazil. Adult patients, with diagnosis of RD, responded to self-esteem, anxiety and depression, health and activation scales. Spearman’s correlation tests, independence tests, mean or median tests, multiple linear regressions evaluated the variables of interest at a significance level of .05.Results: Eighty-six patients (mean age = 45.23, SD = 14.30) were evaluated. High activation (mean = 65.83, SD = 14.20) and self-esteem scores (mean = 30.67, SD = 5.65) were observed, while moderate anxiety scores (mean = 8.21, SD = 4.37) and low scores for depression (mean = 6.37, SD = 3.98). Significant correlations were observed, from low to moderate magnitude, among other measures with activation (p < .05). There were weak correlations between activation and formal study time, self-esteem with age and family income, depression and number of comorbidities diagnosed or self-reported (p < .05). The RD type no affects any of the constructs evaluated.Conclusions: It was concluded that patients with rheumatic diseases presented high self-esteem, moderate anxiety levels and low levels of depression and a high level of activation. Lower number of diagnosed comorbities, higher formal study time was related to better self-management in health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".