Risk factors for depression in older adults in Bogotá, Colombia
Bibliographic record
Abstract
Purpose This paper aims to identify psychosocial, demographic and health risk factors associated with depression in older people. Design/methodology/approach A correlational study with 281 independent and autonomous persons of the community over 60 years old from Bogotá was conducted. The three instruments used to measure the variables included in the data analyses were Demographic and Health Data Questionnaire, Short version of 15 items of Geriatric Depression Scale (GDS) and Montreal Cognitive Assessment Test (MoCA). Findings Fifteen percent of the participants presented depression. Depression was associated with different demographic, low social support and health factors in this population group and was particularly high in women. Being a woman with poor social support networks and a previous history of depressive episodes should be considered as determining factors within a clinical risk profile for depression in older adulthood. It is essential to design prevention strategies focused on women and on the development of better social support in old age. Originality/value Depression is a prevalent and highly disabling disease, when it is suffered by an older person it is associated with higher mortality, functional dependence, poor physical health, worse quality of life indicators and psychological well-being. In the elderly, the clinical diagnosis of depression is difficult, as it has a high comorbidity and is often confused with other health conditions prevalent during older adulthood.
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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 | 0.001 | 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".