Bibliographic record
Abstract
Purpose: The purpose of this study was to explore factors that influence post operative depression in elderly patient. Method: This study was utilized descriptive correlational design. The convenience sample was composed of 82 operational elderly patients in Korea. Stepwise multiple regression was used to identify significant factors influencing postoperative depression in elderly patient. Postoperative depression was measured using the short form Geriatric Depression Scale(GDS), anxiety was measured by the State-Trait Anxiety Inventory, Pain was measured by the Short-Form McGill Pain Questionnaire, family support was measured by Family APGAR. Result: Postoperative depression in elderly patient was significantly influenced by preoperative depression, anxiety, family support. This regression model explained 66%(Adj R2 =.66) of the variances in postoperative depression. Preoperative depression explained 46% of the variances, and anxiety explained 19% of the variances, and family support explained 3% of the variances. Conclusion: Results of this study suggest that nurses intervene more effectively in caring their patients with postoperative patient by recognizing the patients preoperative depression, anxiety, and family support. We suggest that It must be preceded assessment and intervention of preoperative depression in order to intervene postoperative depression.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.047 |
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; both teacher heads agree on what is shown here.
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".