States of Self as Agents of Self-Killing: An Egogram-based Suicide Note Analysis Study in Russia
Bibliographic record
Abstract
The article presents findings of the egogram-based suicide note analysis, which was undertaken by three experts (MDs, PhDs, certified in TA) in a sample of 26 people (36 suicide notes) in Ryazan, Russia, in 2000 and 2017. The results of the study imply that the presuicidal intrapersonal activity is quite diverse and evolving, and may vary between those who complete suicide lethally and those who survive their suicide attempt. Lethal suicides were characterised by elevated levels of Adult and Adapted Child whereas non-lethal suicide attempts showed an apparent increase in Adapted Child and negative Controlling Parent levels. The authors inferred that suicidal individuals with serious lethal intent might maintain moderate levels of Adapted Child (suffering) so as to enable Adult to accumulate energy needed to perform a fatal suicide attempt. In attempted suicides, high levels of negative Controlling Parent targeting relevant others may diffuse the energy necessary for completion of suicide. Attempted suicide egograms were illustrative of the manipulative nature of the non-lethal suicide attempts, whereas completed suicides did not. Egograms of non-lethal suicide attempts and intoxicated completed suicides had similar distribution of ego state levels, which may reflect the effect of alcohol interfering with the activity of protective Parental substructures and strengthening the role of the negative Controlling Parent targeting either one’s inner self or relevant others.Citation - APA format:Shustov, D., Tuchina, O., Agibalova, T., & Zuykova, N. (2018). States of Self as Agents of Self-Killing: An Egogram-based Suicide Note Analysis Study in Russia. International Journal of Transactional Analysis Research & Practice, 9(1), 5-22. https://doi.org/10.29044/v9i1p5
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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 | 0.002 |
| 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 | 0.001 |
| 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".