An Evaluation of Stress and Burnout in Education and Its Impact on Job Performance and Work Life Quality
Bibliographic record
Abstract
Stress and burnout in education is the result of a teacher experiencing unpleasant, negative emotions—such as anger, anxiety, tension, frustration, or depression—that have an impact on their job performance (Carroll et al., 2021). Job performance is identified as the actions or behaviors that are relevant to an organization’s goal and is measured by each individual’s proficiency (Conte & Landy, 2019). Campbell identified multiple factors that contribute to job performance that are declarative knowledge, procedural knowledge, and motivation (Conte & Landy, 2019). Variables can impact and have a direct impact on one’s performance if one of the factors are changed (Conte & Landy, 2019). One of the biggest factors of job performance is burnout and stress and its impact on a teacher’s motivation. Burnout is described as prolonged or chronic job stress that happens over time and is consistent and repeated (Hills, 2019). Burnout is marked by exhaustion; feeling emotionally drained; cynicism/less identification with the job; alienation; and feelings of reduced professional ability. This reduced capacity means that some people do not see any value to what they are doing or contributing (Hills, 2019). What happens to an individual who becomes burned out is that there is the extinction of motivation or incentive to a cause (i.e., the organization) (Hills, 2019). When looking at reasons for burnout/demotivating conditions, several areas of concern are highlighted that include a lack of control, lack of resources, unclear or impossible job expectations, dysfunctional workplace, a mismatch in workplace values, poor job fit, and work–life imbalance (Hills, 2019).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".