Effectiveness of a Selective Advising Program in Reducing the Degree of Compulsive Buying Behavior among Umm Al-Qura Female Students
Bibliographic record
Abstract
The present study is an attempt to investigate the effectiveness of a selective advising program in reducing the degree of Compulsive Buying Behavior among female students, Faculty of Education at Umm al-Qura University. The sample consisted of (200) female students to verify the validity and reliability of the tool. The quasi-experimental method was utilized through two groups (experimental and control). The results indicated high rates of compulsive buying among the participants (18-30) years, where there was a great difference between the items of the scale and a great desire for buying for many reasons. The study results showed that there are statistically significant differences between the mean scores of experimental and control groups at the pre- and post-test in the compulsive buying scale, significant at (0.005) in favor of the pre- and post-test. There are also statistically significant differences between the mean scores of the experimental group at the pre- and post-test in the compulsive buying scale, significant at (0.005) in favor of the post-test, i.e. the selective advising program decreased the rate of Compulsive Buying Behavior. The results also showed that there are no statistically significant differences between the mean of the experimental group ranks in the post- and follow-up test, which reveals that the advising program is being effective.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".