A correlative study of uncertainty in illness and alexithymia among the elderly with chronic diseases in nursing home
Bibliographic record
Abstract
Objective To investigate the level of uncertainty in illness and alexithymia of the elderly with chronic diseases in nursing home and analyze the correlation between them. Methods Totally 198 elderly residents from 5 nursing homes in Taiyuan from January to April 2017 were investigated using demographic data questionnaire, Mishel Uncertainty in Illness Scale (MUIS) , and Toronto Alexithymia Scale (TAS-20) . Results The total score of MUIS and TAS-20 were (97.11±8.83) and (59.64±6.71) . There were statistically differences in MUIS scores among patients with different ages, medical insurances, diseases, and courses of disease (P<0.05) . The total and each dimension scores of MUIS positively correlated with the total and each dimension scores of TAS-20 (P<0.01) . Conclusions The higher of the level of uncertainty among elderly patients with chronic diseases, the more serious alexithymia they will have. Nursing staff could take effective measures from the aspect of alexithymia to relieve the uncertainty in illness among the elderly with chronic diseases. Key words: Aged; Chronic diseases; Uncertainty in illness; Alexithymia; Nursing home
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".