Exploring the Links between Depression, Integrity, and Hope in the Elderly
Bibliographic record
Abstract
OBJECTIVE: To explore the links between depression, integrity, and hope in the elderly. METHOD: For this pilot study, we recruited a voluntary sample of cognitively intact elderly patients receiving psychiatric care (n = 35). Recruitment sources included an inpatient geriatric psychiatry unit (n = 14), a geriatric psychiatry day program (n = 6), and an outpatient geriatric psychiatry service (n = 15). Participants completed a questionnaire designed to measure depression, acording to the Geriatric Depression Scale Short Form (GDSSF); integrity, according to the Sense of Coherence Scale-Short Form (SOC-13); and hope, according to the Hope Differential-Short (HDS) and the Hope Numerical Rating Scale (Hope-NRS). The HDS consists of 3 separate subscales: Personal Spirit, Risk, and Authentic Caring. We analyzed the data, using descriptive statistics, t tests, and Pearson correlations. RESULTS: Patients with no depression (n = 17) showed a greater sense of coherence (SOC) (P < 0.01), higher levels of hope (Hope-NRS, P < 0.05), enhanced Personal Spirit (HDS subscale, P < 0.05), and greater risk taking (HDS subscale, P < 0.01) than did patients with depression (n = 18). The 2 variables that correlated most highly with depression were SOC (r = -0.65, P < 0.01) and Risk (HDS subscale, r = -0.62, P < 0.01). CONCLUSIONS: These findings suggest that depression, integrity, and hope are highly interrelated in the elderly population and may influence mastery of the developmental tasks of aging. Further research is warranted to better understand these complex experiences in late life.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".