Analyzing Predictive Role of Pre-Service Teachers’ Occupational Anxiety Level on Positive Emotions
Bibliographic record
Abstract
The objective of this study is to examine the predictive role of pre-service teachers’ professional anxiety on positiveemotions. The relational screening model was adopted in the study. 484 pre-service teachers were selected from theFaculty of Education in Kırıkkale University with stratified sampling method for sample. Occupational AnxietyScale and Dispositional Positive Emotion Scales were applied to sample group. Correlation and multiple regressionanalysis were used to analyze the data. Correlation analysis revealed that the relations between contentment andoccupational exam centered anxiety and socio-economic centered anxiety have the highest scores, whereas therelations between compassion and socio-economic centered anxiety and school management centered anxiety havethe lowest scores. Results of multiple regression analysis indicated that occupational exam centered anxiety andsocio-economic centered anxiety are significant predictors of positive emotions but job oriented anxiety, interactionwith students centered anxiety, colleagues and students’ parents centered anxiety, self-development centered anxiety,adaptation self-development centered anxiety and school management centered anxiety isn’t a predictor for positiveemotions. Suggestions for decreasing the pre-service teachers’ occupational anxiety level were presented at the endof the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".