Bibliographic record
Abstract
The purpose of this study was to predict addiction tendency in adolescents based on their parents' emotional orientation.The present study was descriptive and correlational.For this purpose, 150 students from high school students of first grade high schools in Tehran province were selected as multistage cluster sampling.In order to measure the variables of the research, two questionnaires, Panass's emotional orientation and the diagnosis of addicted subjects were used.Data were analyzed using SPSS-21 software.The results of regression analysis showed that the negative emotional orientation subscales had a predicted contribution of 0.310.Regarding the contribution of each of the variables, one increase in the negative emotional-score score of 0.22 points increases the tendency toward addiction.Based on the findings of this study, it can be concluded that the improvement of relationships between family members, especially parents, can turn the home environment into a safe and lovely environment for children, and no doubt such a family is less likely to be addicted.Therefore, investing in education and raising the awareness of the various strata of the community about addiction and its risks is much more profitable than investment for the treatment of addicts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.779 | 0.677 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".