https://www.clausiuspress.com/assets/default/article/2023/11/15/article_1700056764.pdf
Bibliographic record
Abstract
Symptom Checklist-90 (SCL-90) was used to investigate the current status of psychological health of 222 teachers randomly selected from 40 special education schools or special education centres in Yunnan Province, to systematically investigate the level of psychological health status of special education teachers and the influence of demographic characteristics on the level of psychological health. The results indicate that: the psychological health of special education teachers in Yunnan Province in terms of SCL-90 scores on each factor and total scores was significantly lower than the national norm, higher than that of primary and secondary school teachers, and lower than that of early childhood teachers in Yunnan Province. The top three psychological problems were obsessive-compulsive symptoms, interpersonal sensitivity symptoms, and depressive symptoms. The psychological health of special school teachers in Yunnan Province is not optimistic. The relevant departments should pay attention to the psychological health of special education teachers and strive to improve the psychological health of special education teachers through various channels and ways.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.749 | 0.701 |
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; the direct Gemma label and the distilled Codex classifier 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".