Survey on the Sustainable Well-Being of Primary School Teachers Based on the OECD Research Framework in Guangdong Province
Bibliographic record
Abstract
This quantitative study investigates primary school teacher well-being in Guangdong Province, grounded in the OECD research framework and Seligman’s positive psychology theory. The study aims to assess teacher well-being dimensions and sustainable outcomes and to explore differences based on demographic and employment factors. A stratified random sample of 400 teachers completed a validated questionnaire covering Cognitive Well-Being (CWB), Subjective Well-Being (SWB), Physical and Mental Well-Being (PMWB), Social Well-Being (SOWB), and Self-Rated Well-Being (SRWB). The results yielded a KMO value of 0.928 and Cronbach’s alpha of 0.812. The findings show high levels of CWB and SOWB, reflecting effective goal achievement, classroom management, and professional relationships. However, SWB, PMWB, and SRWB were moderate, indicating issues related to stress, physical strain, and workload concerns. SWB was found to be a strong predictor of sustainable well-being (r = 0.921, p < 0.001), followed by SRWB and SOWB. Demographic and employment factors influenced well-being outcomes. Public school teachers experienced educators, and those with stable employment and higher incomes reported better outcomes. No significant differences in PMWB were observed across groups. These findings highlight the need for targeted interventions to promote emotional resilience and equitable support for teachers.
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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 | 0.001 |
| 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".