A study on self-rating health condition and related factors in hospitalized depressive patients
Bibliographic record
Abstract
Objective To investigate self-rating health condition and related factors in hospitalized patients with depression.MethodsA total of 105 patients with depression and 120 healthy controls were assessed with a self-designed questionnaire,self-rating health measurement scale version 1.0(SRHMS),toronto alexithymia scale(TAS) and self-rating depression scale(SDS).T test,correlation and regression analysis were carried out.Results(1) Compared with controls,patients with depression showed significant lower scores in the total score of SRHMS and three factors(P0.05 or P0.01).(2) There were many factors such as the total scores of SDS,TAS and its factors showed significant correlation with total score and most factor scores of SRHMS in patients with depression(r=0.227～0.507,P0.01).(3) Total scores of SDS,economic status,confidence of therapy,general therapeutic effect and stress of marriage-domesticity entered the regression equation for the total socre of SRHMS in patients with depression by turns.ConclusionPatients with depression suffered from bad health status.The total scores of SDS,economic status,confidence of therapy,general therapeutic effect and stress of marriage-domesticity may be responsible for bad health status in patients with depression.
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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 | 0.000 | 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 | 0.000 |
| 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".