Effect of characteristic factors of pension institutions on alexithymia of the elderly
Bibliographic record
Abstract
ObjectiveTo understand the current situation of alexithymia among the elderly in pension institutions and analyze the effect of characteristic factors of pension institutions on alexithymia among the elderly.MethodsSeven pension institutions were selected by cluster sampling in Tangshan city.A total of 517 elderly people who met included criterion,were surveyed by using the general situation questionnaire,Characteristic Factor Questionnaire in Pension Institutions and Toronto Alexithymia Scale(TAS⁃20).ResultsThe incidence of alexithymia among the elderly in pension institutions was higher,with a total score of 58.74±7.23.Multivariate stepwise regression analysis showed that educational level,institutional nature,cleanliness,nutritional mix,visiting frequency,collective activities and living well⁃being affected the occurrence of alexithymia among the elderly in institutions.ConclusionMedical staff in pension institutions should pay attention to the occurrence of alexithymia among the elderly.They should take corresponding intervention measures on characteristics of the pension institutions,in order to reduce the occurrence of alexithymia.
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 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 | 0.002 |
| 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) | 0.002 | 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".