The End of Leisure: Are Preferred Leisure Activities Contraindicated for Education-Related Stress/Anxiety Reduction?
Bibliographic record
Abstract
Teacher stress is an increasing problem not only for practicing teachers but for student teachers as well. It leads to professional teachers leaving the profession, and future teachers enduring much stress and anxiety throughout teacher education programs. To further explore effects of stress, teacher candidates were surveyed with respect to (1) the role of their leisure preferences and (2) their stress levels related to Pedagogy, Evaluation, Class Management, and Interpersonal Relations. In Study One ( n=216 ), a profile of leisure preferences was comprised, and findings from the relationship between leisure preferences and teaching anxieties contributed to a profile to explore reduced anxiety over time. A follow-up investigation (Study Two, n=136 ) tested the discriminatory potential of these leisure profile variables to separate those who showed less anxiety over time from those who regressed. Surprisingly, increased anxiety was associated with higher leisure in Sports, Adventure, Travel, and Exotica and with non-Science majors, Human Kinesiology majors, and Males. Some leisure preferences appear to be counterintuitive, given commonsense notions of the value of leisure. A Leisure Preferences Profile serves to facilitate discrimination between groups (improvement in anxiety levels versus no improvement) with respect to Pedagogical and Evaluation anxiety. A Composite Profile suggests that Leisure preferences related to Sports, Adventure, and Exotica are counterproductive in reducing stress related to Pedagogy. Implications 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".