Suicide as a Social Problem among Young People and Adolescents in Aktobe City
Bibliographic record
Abstract
The purpose of this article is to study the cause-effect relationship leading to suicide based on the results of two questionnaires conducted in Marat Ospanov State Medical University (Aktobe city). Method: The study involves two questionnaire surveys (2016 and 2017 years) of 1,000 1st-4th year students of the West Kazakhstan Marat Ospanov State Medical University based on the Beck Scale for Suicidal Ideation (aimed at determination of the level of depressive state, the portrait of a suicide) and the second similar questionnaire involved 500 respondents of the same University using the Dembo-Rubinstein method in Prikhozhan’s modification, that is aimed at determining the level of self-esteem and aspiration. Results: The questionnaires conducted in Marat Ospanov State Medical University allowed to determine the levels of suicidal behaviour among young people and adolescents of Aktobe city as well as determine the criteria, levels and reasons of suicidal intentions in percentage and by gender criterion. The results are partially presented in the form of diagrams. Conclusions: The study may be used for developing the complex methodologies of suicide prevention among young people and adolescents in Aktobe city and everywhere in the world where such a problem arises sharply. Such a kind of study was firstly conducted with usage of declared methods in Aktobe city.
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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 | 0.001 | 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 | 0.000 |
| 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".