UNA REVISIÓN DE LOS FACTORES QUE CONTRIBUYEN A LA DEPRESIÓN EN LOS ÚLTIMOS AÑOS DE VIDA
Bibliographic record
Abstract
Literature provides a wide variety of information about food intake, physical illness, and psychological disorders among the aging population.Late-onset of depression is one of the most common mental health problems in adults aged 60 or older.The primary purpose of this paper is to investigate the relationship between late-life depression and nutrition intake among older adults.Secondly, literature has indicated that late-life depression is influenced by genetic, situational, illness-related biological and psycho-social factors.However, late-life depression, relative to earlyonset depression, appears to be less influenced by genetics and more influenced by environmental factors.Psychological models postulate that late-life depression arises from the loss of self-esteem, loss of meaningful roles, loss of significant others, decline of social contacts, reduction of physical ability, financial difficulties and decline in coping skills.For these reasons, the contributing social, physical and psychological factors are briefly investigated in relation to nutritional aspects.Therefore, the scope of this paper will examine the social, physical, and psychological issues that directly or indirectly affect food intake and consequently depression in the elderly population.Key words: Late-
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".