Effect of Self-efficacy on the Relationship between Corporal Punishment and School Dropout
Bibliographic record
Abstract
Corporal punishment results in aggressive behaviours in students. As a result, some students leave school. Researchers believe that the issue of school dropout can be reduced by adopting different motivational techniques. Self-efficacy is one technique that can be promoted to create a caring and supportive learning environment. The issue of dropout is alarming in many Pakistani schools especially at the primary level due to the prevalence of corporal punishment and other factors. This issue prevails mostly in government schools where teachers adopt stringent steps in the teaching process. This study was specifically designed to examine the role of self-efficacy as a moderator between corporal punishment and school dropout. Many studies have explored the relations of corporal punishment with school dropout. It remains to be seen what actually moderates these relations. This study contributes to this gap in the literature by examining self-efficacy as an influencing factor. The study used a validated questionnaire to survey 300 government primary school teachers’ attitude on this issue. Results of the study indicated that corporal punishment significantly correlated with school dropout rate. Secondly, self-efficacy significantly moderates the relations between corporal punishment and school dropout. On the basis of these findings, the study concludes that teachers may reduce the issue of school dropout by creating a supportive and caring teaching and learning environment in school. Finally, the study suggests that the school administration play a key role to overcome the issue of increasing rate of school dropout by adopting rules and procedures to convince and motivate teachers to avoid corporal punishment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".