P4‐150: Suicidal ideation and changes of cognition, mood, aggression, and sleep quality under the age of 50 as the risk factors of elderly life
Bibliographic record
Abstract
The changes in cognition, mood, aggression and sleep under th age of 50 is the assumption that may be a risk factor that can affect the lives of future seniors. It is to investigate about the suicidal ideation on changes of cognition, mood, aggression, and sleep quality under the age of 50 for prediction of elderly life. Adult population under 50 years old in Jeonju city of Korea, randomly collected data Demographic data, Scale for suicidal ideation (SSI-Beck), Mini mental state examination-Korean version (MMSE-K), Montreal cognitive inventory-Korean version (Moca-K), Beck depression Inventory (BDI), Korean version of mood disorder questionnaire (K-MDQ), Pittsburgh sleep quality Index (PSQI), Buss & Durkee Hostility Inventory (BDHI), IBM SPSS Statistics 22 version, Hypothesis testing by multiple regression analysis (dependent variable-suicidal ideation, independent variables-cognitive changes, mood changes, aggression, sleep quality), Informed consent for research questionnaires. N=95, Sex [men 43 (45.3%), women 52 (54.7%)], Age (mean, 47.232±11.2273, men 48.140±11.1667, women 46.481±11.3301), Marital Status [(unmarried 30 (31.6%), married 53 (55.8%), divorced 7 (7.4%), bereaved 5 (5.3%)], Education [elementary 6 (6.3%), middle 16 (16.8%), high 36 (37.9%), college 37 (38.9%)], Occupation [professional 15 (15.8%), self-business 10 (10.5%), official 3 (3.2%), laborer 9 (9.5%), housewife 18 (18.9%), religious 5 (5.3%), student 7 (7.4%), unemployed 28 (29.5%)], Height (men 1.693±0.06900 cm, women 1.5790±0.05266 cm, mean 1.6307±0.08306), Body weight (men 67.07±12.533 kg, women 58.23±6.659 kg, mean 62.23±10.668 kg), BMI (Body mass index) men 23.3067±3.41420, women 23.4158±3.01675, mean 23.3664±3.18575, cardiovascular history [no 82 (86.3%), yes 13 (13.7%)], Diabetes mellitus [no 81 (85.3%), yes 14 (14.7%)], Hypertension [no 79 (83.2%), yes 16 (16.8%)], Cerebrovascular disease [no 88 (92.6%), yes 7 (7.4%)], Neuropsychiatric History [no 67 (70.5%), yes 28 (29.5%)], Alcohol [no 66 (69.5%), past yes 14 (14.7%), present yes 15 (15.8%)], Tobacco [no 62 (65.3%), past yes 16 (16.8%), present yes 15 (15.8%)], Dementia (in family) [no 74 (77.9%), yes 21 (22.1%)], Multiple regression analyses–Pearson correlation analysis (1 tailed), SSI-Beck/BDI .533 (p=.000), SSI-Beck/PSQI .336 (p=.000), SSI-Beck/BDHI .387 (p=.000), BDI/PSQI .572 (p=.000), BDI/BDHI .281 (p=.003), BDI/Moca-k -.183 (p=.038), BDI/MMSE-K -.289 (p=.002) K-MDQ/PSQI .211 (p=.020), Moca-K/MMSE-K .575 (p=.000), MMSE-K/PSQI -.293 (p=.002), Model 1[R .615, R square .378, Predictors (cognition MMSE-K, Moca-K, Mood change (K-MDQ, BDI), Aggression (BDHI), Sleep quality (PSQI), Dependent variable (suicidal ideation)], ANOVA-Model 1 (F=8.907, p=.000), Coefficients-Model 1 (suicidal ideation, SSI-Beck, t=-2.709, p=.000). The above results suggest that the complicated changes of cognition, mood, aggression, and sleep quality under the age of 50 as risk factors in elderly life can induce suicidal ideation. Hence as close to the old age, it should be recognized the seriousness about cognitive change, mood change, aggression, and sleep quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".