Working Community-Related Interaction Factors Building Occupational Well-Being – Learning Based Intervention in Finnish and Estonian Schools (2010–2013)
Bibliographic record
Abstract
This article examines whether a three-year learning-based work community intervention resulted in changes in working community-related interaction factors and occupational well-being among Finnish and Estonian school staff. It reports the types of changes in working community-related interaction factors and their associations to the subjective occupational well-being and general occupational well-being of the working community. The initial quantitative survey data were collected in Finland (n = 486) and Estonia (n = 1330) in 2010 using the “Well-being at your work index questionnaire.” The same measurement tool was used in 2013 to collect final survey data from school staff members in Finland (n = 545) and Estonia (n = 974). The data were analyzed statistically with percent, mean, SD, Mann-Whitney test, sum variables, one-way analysis of variance and Spearman’s correlation. Changes were detected in factors related to working community interaction; in particular, statistically significant changes in work management and time use were detected in Finnish schools. Working atmosphere and appreciation of others’ work, cooperation and information, and work management and time use were associated to both the subjective occupational well-being and general occupational well-being of the working community. Schools should plan and implement development activities to promote the subjective occupational well-being and general working community occupational well-being. Development work should focus on working community-related interaction, such as trust between workers. Principals should draw particular attention to principal–subordinate relationships and to providing information about changes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".