A study of quality of life and scale of happiness on the patient with insomnia
Bibliographic record
Abstract
Objective:To investigate the quality of life and scale of happiness of the patients with insomnia.Methods:The inquiry of quality of life and memorial university of Newfoundland scale of happiness were used to survey the quality of life and scale of happiness of 60 cases with insomnia.The control group consisted of 30 normal individual without physical disorder,matched in age and sex.Results: The score of the patients with the short-term insomnia was statistic difference than the normal control group(P0.05);the score of the patients with short-term insomnia was significantly statistic difference than the chronic insomnia(P0.01),the score of patients with chronic insomnia was significantly statistic difference than the normal control group(P0.01) separately,especially in long-term insomnia patients.Conclusions: The related factors influence the quality of life and scale of happiness of the patients with insomnia are mental health status,habits and customs,housing conditions,physical strength,life satisfaction rating scale.The patients with insomnia showed more psychological problems than control group.The quality of life and scale of happiness could be improved through attaching great importance to physical disorders of patients with insomnia,improving housing conditions and so on.A synthetically measures was been adopted in order to rising the quality of life and scale of happiness of the patients with insomnia.
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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 | 0.002 |
| 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 | 0.000 | 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".