Alexitimia y agresión en adolescentes: sus diferencias de género
Bibliographic record
Abstract
Objective. To know the differences in alexithymia and aggression between male and female adolescents in the fourth and fifth year of high school education. Methods. Level of descriptive-comparative research, type of basic research, transversal design. We worked with a sample of 374 high school students, considering the non-probabilistic sampling of census type that selected 173 men and with probabilistic sampling of stratified type were considered 201 women. The Toronto Alexithymia Scale (T.A.S.-20) and the Buss Perry Aggression Questionnaire were used as instruments for data collection. Likewise, the difference in proportions was used for data analysis. Results. When comparing the differences with alexithymia, according to the gender of the students, a P value = 0.530 was obtained, this being greater than 0.05; so the null hypothesis is accepted and it is stated that there are no significant differences in alexithymia between male and female adolescents. On the other hand, when comparing the differences with respect to aggression according to gender, a P value = 0.001 was obtained, this being less than 0.05; therefore, the null hypothesis of equality is rejected and it is affirmed that there are differences in aggression between male and female adolescents. Conclusions. There is a relationship between alexithymia and aggression in male and female adolescents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".