School climate and social–emotional learning: Predicting teacher stress, job satisfaction, and teaching efficacy.
Bibliographic record
Abstract
The aims of this study were to investigate whether and how teachers' perceptions of social-emotional learning and climate in their schools influenced three outcome variables--teachers' sense of stress, teaching efficacy, and job satisfaction--and to examine the interrelationships among the three outcome variables. Along with sense of job satisfaction and teaching efficacy, two types of stress (workload and student behavior stress) were examined. The sample included 664 elementary and secondary school teachers from British Columbia and Ontario, Canada. Participants completed an online questionnaire about the teacher outcomes, perceived school climate, and beliefs about socia-emotional learning (SEL). Structural equation modeling was used to examine an explanatory model of the variables. Of the 2 SEL beliefs examined, teachers' comfort in implementing SEL had the most powerful impact. Of the 4 school climate factors examined, teachers' perceptions of students' motivation and behavior had the most powerful impact. Both of these variables significantly predicted sense of stress, teaching efficacy, and job satisfaction among the participants. Among the outcome variables, perceived stress related to students' behavior was negatively associated with sense of teaching efficacy. In addition, perceived stress related to workload and sense of teaching efficacy were directly related to sense of job satisfaction. Greater detail about these and other key findings, as well as implications for research and practice, are discussed
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".