Factores asociados a la depresión en pacientes con atención primaria de la salud en Cusco y Abancay
Bibliographic record
Abstract
-- The research examines the factors that are associated with depression inpatients that are cared in primary care facilities ofCusco and Abancay. This also allows us know comparatively general levels of depression, their characteristics by gender, origin, life stage, education level and occupation. We found that the place of origin, educational attainment and occupation, are significant factors associated with depression (p = 0.00 in all three cases). Only one quarter part ofthe total sample does not exhibit the problem, the rest reflects some level of depression, mild depression has the highest incidence, followed by moderate depression and severe than is presented in the fifth of the total study sample. There are higher percentages all levels of depression in the sample subjects from Cusco, in the case of serious level, the presence of depression is twice in Cusco compared to Abancay. At all levels, the depression occurs with highest incidence in the sample of male cusquenians compared to male abanquineans, and in the female cusquenians compared to female abanquineans. Mild depression is higher in adolescents from Abancay compared to cusquenians adolescents, but is higher in adults from Cusco compared to abanquineans adults. Moderate depression is similar in both samples and severe depression is higher in adolescents and adults from Cusco. In general, depression is higher in persons ofschool education, compared to persons of professional education; and is lower in persons with stable work compared with persons who studyor have temporary work.\n\n-- Keywords: depression, depression levels, associated factors to depression, Beck Depression Inventory (BDI).
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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 | 0.001 |
| 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".