An Investigation of the Relationship between Married Teachers’ Perception of Sacrificing Behavior, Alexithymia, and Marital Burnout
Bibliographic record
Abstract
The present research was undertaken to investigate the relationship between spouses’ perception of sacrificing behavior, alexithymia, and marital burnout among primary school teachers. The statistical population of the study was comprised of all primary school teachers in Amol from among whom 200 teachers were selected through random multi-stage cluster sampling method. The instruments included Perception of Sacrifice Scale (Harper & Figuerres, 2008), Toronto Alexithymia Scale (1994), and Couple Burnout Measure (Pines, 1996). The data were analyzed using Pearson correlation and multiple regression analysis. The findings indicated that spouses’ perception of sacrificing behavior was negatively associated with marital burnout, whereas alexithymia was positively correlated with marital burnout (P<0.001). Regression analysis also showed that spouses’ perception of sacrificing behavior could negatively predict marital burnout, while alexithymia positively predicted this variable. Among predictor variables, alexithymia was the most important predictor of marital burnout. Based on the findings, school psychologists and counselors can use life skills training programs to enhance sacrificing behavior and reduce problems caused by alexithymia among teachers and as a result decrease teachers’ marital burnout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".