Depressive symptoms, self-esteem, HIV symptom management self-efficacy and self-compassion in people living with HIV
Bibliographic record
Abstract
The aims of this study were to examine differences in self-schemas between persons living with HIV/AIDS with and without depressive symptoms, and the degree to which these self-schemas predict depressive symptoms in this population. Self-schemas are beliefs about oneself and include self-esteem, HIV symptom management self-efficacy, and self-compassion. Beck's cognitive theory of depression guided the analysis of data from a sample of 1766 PLHIV from the USA and Puerto Rico. Sixty-five percent of the sample reported depressive symptoms. These symptoms were significantly (p ≤ 0.05), negatively correlated with age (r = -0.154), education (r = -0.106), work status (r = -0.132), income adequacy (r = -0.204, self-esteem (r = -0.617), HIV symptom self-efficacy (r = - 0.408), and self-kindness (r = - 0.284); they were significantly, positively correlated with gender (female/transgender) (r = 0.061), white or Hispanic race/ethnicity (r = 0.047) and self-judgment (r = 0.600). Fifty-one percent of the variance (F = 177.530 (df = 1524); p < 0.001) in depressive symptoms was predicted by the combination of age, education, work status, income adequacy, self-esteem, HIV symptom self-efficacy, and self-judgment. The strongest predictor of depressive symptoms was self-judgment. Results lend support to Beck's theory that those with negative self-schemas are more vulnerable to depression and suggest that clinicians should evaluate PLHIV for negative self-schemas. Tailored interventions for the treatment of depressive symptoms in PLHIV should be tested and future studies should evaluate whether alterations in negative self-schemas are the mechanism of action of these interventions and establish causality in the treatment of depressive symptoms in PLHIV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".