Personality, child maltreatment, and substance use: Examining correlates of deliberate self-harm among university students.
Bibliographic record
Abstract
La majorite des recherches recentes sur l'automutilation (AM) reposaient sur des echantillons cliniques et ont etabli des correlations psychologiques ou cliniques, negligeant ainsi les facteurs generaux lies a la personnalite. La presente etude a examine les traits de personnalite, la violence durant l'enfance et la consommation de drogues comme correlats de l'AM dans un echantillon de 319 etudiants a l'universite (65,2 % de femmes). Un des buts etait de decrire la nature de l'AM parmi les etudiants. Dans l'ensemble, 29,4% d'entre eux ont dit avoir recouru au moins une fois a l'AM, et les taux d'AM etaient semblables chez les femmes et les hommes. La methode d'AM la plus courante etait la coupure, surtout chez les femmes. Les hommes avaient ete plus souvent portes a se placer dans une situation violente ou les risques de blessure etaient eleves. On n'a constate aucune difference dans le nombre de methodes d'AM rapportees par les deux sexes. Des regressions multiples hierarchiques ont revele plusieurs correlats positifs de l'AM, dont un nombre accru de symptomes de la depression, une plus grande ouverture a l'experience et recherche de sensations fortes, des antecedents de violence psychologique et la consommation de drogues illicites. Ces resultats ont d'importantes repercussions sur l'evaluation et le traitement d'etudiants a l'universite pratiquant l'automutilation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".