Occupational Well-Being: A Structural Equation Model of Finnish and Estonian School
Bibliographic record
Abstract
This study aimed to test the original Occupational Well-being of School Staff Model (OWSS Model) from 2005. This model was tested using data collected in two stages (in 2010 and in 2013) from school staff in Finnish and Estonian public primary and secondary schools. In 2010, there were 486 Finnish respondents (Finnish study 1), and in 2013, there were 545 Finnish respondents (Finnish study 2). Correspondingly, there were 1330 Estonian respondents in 2010 (Estonian study 1), and 974 Estonian respondents in 2013 (Estonian study 2). Based on structural equation modelling, Finnish data from 2010 and 2013 suited the OWSS Model well. Based on Estonian data from 2010 and 2013, the model was slightly improved, but its main structures remained largely unchanged. On the whole, the results support the previous notion that the occupational well-being of school staff should be examined with reference to a broad spectrum of four viewpoints covering working conditions, worker and work, the working community and professional competence. General occupational well-being of the working community and subjective occupational well-being were best explained by working atmosphere and appreciation of others’ work, especially in Finland. In Estonia, occupational well-being was best explained by working atmosphere and appreciation of others’ work and working space, postures and equipment. Long-term testing with data from two countries and from two different testing periods confirmed that the model may continue to be applied in school contexts for planning, implementation and evaluation of occupational well-being, as well as for promoting public health.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".