P2‐504: GERIATRIC DEPRESSION AND ALEXITHYMIA IN FAMILY CAREGIVERS OF PATIENT WITH DEMENTIA: A CROSS SECTIONAL STUDY
Bibliographic record
Abstract
Caring for patients with dementia is a stressful process for caregivers which increases the risk of physical and mental problems, among which depressive disorders stand out. Aim: To determine the prevalence and predictors of Geriatric Depression and Alexithymia in elderly people. A cross sectional study was conducted. A non-probability, purposive sampling strategy was used. The sample was comprised of 230 elderly adults of both sexes. We compared two groups: family caregivers of patients with dementia (n = 115) and non-caregivers (n = 115). They were evaluated in individual interviews using the following measures: a socio-demographic questionnaire (ad hoc), an adaptation of the Geriatric Depression Scale created by Yesavage (V-15) and the Latin American Consensual Toronto Alexithymia Scale LAC (TAS-20). Measures of central tendency and of dispersion were obtained to describe the socio-demographic variables and Geriatric Depression and Alexithymia, and the Spearman's rho correlation coefficient was used to measure the degree of association between Geriatric Depression and Alexithymia. Two logistic regression models were constructed to assess the association between predictors with alexithymia and depression. An error probability minor or equal to 0.05 was established. Data were analyzed by SPSS statistical software version 21.0. The median age was 71 years (IQR 66 to 77), the participants were mostly women (72.5%), married (67.05%), with secondary education (31.6%) and retired (64.7%). The median GDS score was 2 (IQR 1 to 5) for non-caregivers and 5 (IQR 3 to 7) for caregivers, p value 0.001. In the multivariate model to determine predictors of depression, those who were caregivers had almost four times the chance of being depressed, OR 3.73 (CI95% 2.00 – 6.94; p value 0.000). We also found a correlation between the presence of Alexithymia and Geriatric Depression (Spearman's rho= 0.38, p value 0.0001). Family caregivers are underdiagnosed patients. Assessing levels of Geriatric Depression on its initial stages allows for proper diagnoses and treatment, in order to preserve the family caregiver's well-being. Therefore, the evaluation of these subjects in parallel with the patient's medical consultation is recommended.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".