A Study of the Present Situation of Occupational Well-Being among Kindergarten Teachers<br>&#8212;Based on OECD &#8220;Teachers&#8217; Well-Being: Data Collection and Analysis Framework&#8221;
Bibliographic record
Abstract
Drawing from the OECD’s “Teachers’ Well-being: Data Collection and Analysis Framework”, this study investigates the current state of occupational well-being among 394 kindergarten teachers in Suzhou. It reveals that the overall level of occupational well-being among kindergarten teachers is at the middle level, with the highest social well-being and the lowest physical and mental well-being; There are significant differences in the occupational well-being of kindergarten teachers in terms of teaching experience and professional titles. Kindergarten teachers with more than 16 years of teaching experience have the highest occupational well-being, while kindergarten teachers with less than 5 years of teaching experience have significantly lower occupational well-being than other teachers; Senior kindergarten teachers have the highest sense of occupational well-being, while kindergarten teachers with second level titles have significantly lower levels of occupational well-being compared to other teachers. Based on research data, in order to enhance the occupational well-being of kindergarten teachers, this study suggests paying attention to the physical and mental well-being, and strengthening the health support system; inspiring a sense of well-being throughout the entire career process and optimizing the career development strategies for kindergarten teachers; creating a good professional environment for kindergarten teachers to reduce their workload and enhance their abilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".