Career Decision Regret as a Predictor: Do Teachers and Administrators Regret Due to Their Career Choice?
Bibliographic record
Abstract
This research aims to explore the relationship between career decision regret, job satisfaction and life satisfaction aswell as determining whether career decision regret significantly predicts job satisfaction and life satisfaction. Havinga descriptive research design, the research has employed relational survey model. The population of the researchholds a total of 7671 teachers and administrators who work at pre-school education institutions, primary, secondaryand high schools located within the central provinces of Kahramanmaraş. The research sample consists of 365participants who have been selected through a random sampling method. The research has deployed “CareerDecision Regret Scale”, “Job Satisfaction Scale” and “Satisfaction with Life Scale”. Independent groups t-test,one-way ANOVA test, Pearson correlation and regression analysis have been used during data analysis. Researchresults have revealed that teachers’ and school administrators’ career decision regret is at “a little” level; they haveexperienced a medium level of general, intrinsic and extrinsic satisfaction; they are at the level of “neither agree nordisagree" in terms of life satisfaction; there is a negative and medium level relation between career decision regretand life satisfaction and between career decision regret and job satisfaction; a significant, positive and medium levelrelation between job satisfaction and life satisfaction; career decision regret is a significant predictor of jobsatisfaction and life satisfaction; 25% of the total variance of job satisfaction and 22% of the total variance of lifesatisfaction level of teachers are explained by career decision regret.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".