The Study of Relations between Life Satisfaction, Burnout, Work Engagement and Hopelessness of High School Students
Bibliographic record
Abstract
The purpose of this research is to determine the relations between high school students’ life satisfaction, burnout, work engagement and hopelessness scores and examine the contribution of their burnout, work engagement and hopelessness scores in the prediction of their life satisfaction scores. The Satisfaction with Life Scale (SWLS), Maslach Burnout Inventory–Student Survey (MBI-SS), Utrecht Work Engagement Scale–Student Survey (UWES-SS), Beck Hopelessness Scale (BHS) and “Personal Information Form”, has been applied on a total of 461 students, 225 (%48.8) of them being girl students and 236 (%51.2) of them being male students, who were continuing the 12th grade in varying high school classes during the 2011-2012 school year within the provincial boundaries of the Mersin Municipality and had voluntarily accepted to participate in the research. Confirmatory factor analysis (CFA), correlation analysis and multiple regression analysis have been used in the analysis of the data. It is observed as a result of the analyses that high school students’ life satisfaction scores have a negative relation with exhaustion, cynicism, efficacy and hopelessness scores; on the other hand, these have a positive relation with vigor, dedication and absorption scores. Also it has been observed that life satisfaction scores only predict hopelessness, absorption and efficacy in a meaningful way.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".