Prevalence of Geriatric Depression and Alexithymia and their association with sociodemographic characteristics in a sample of elderly persons living in Buenos Aires, Argentina
Bibliographic record
Abstract
Abstract Objective: to evaluate the prevalence of Geriatric Depression and Alexithymia and their association with sociodemographic characteristics in independent elderly persons without known depression. Method: a cross-sectional study was conducted, based on a non-probabilistic, intentional type sampling strategy. A total of 176 independent men and women aged over 60 years residing in the city of Buenos Aires, Argentina, were evaluated through individual interviews using the following instruments: a sociodemographic (ad hoc) questionnaire, an adapted version of the questionnaire of the Yesavage Geriatric Depression Scale (V-15) and the Latin American Alexithymia LAC TAS-20 Scale. The Chi-squared and Student's t-tests were used and the Odds Ratio was calculated, with a probability of error less than or equal to 0.05. Results: The mean age was 73 years (+7.1 years) and 72.7% of the participants were women. The prevalence of Geriatric Depression was 35.8%, while that of Alexithymia was 50.6%. The presence of Geriatric Depression was significantly associated with the female gender and with individuals who did not work. High Alexithymia values were observed among those with primary education and a low occupational level. Conclusion: The evaluation of Geriatric Depression and Alexithymia in clinical care is recommended, and the social determinants of the health of the elderly should also be considered in the diagnosis and treatment of these conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".