Neurocognitive Game between Risk Factors, Sleep and Suicidal Behaviour
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
INTRODUCTION: Sleep and lifestyles interact to allow the appropriate development of cerebral structures, and prevention of mood disorders. But just a hand of articles identified a precise relationship between these two above, and the probability to develop a suicidal behaviour. OBJECTIVE: The aim of this study is to explore how the suicidal behaviour is associated in simultaneous with sleep components, psychological stress, depression, anxiety, well-being, addiction, and global health of participants; and if it is also influenced by the sociodemographic profile of each subject. METHODS: The present study was led by a questionnaire incorporating McNair test, and an incorporated score to evaluate suicide tendencies. The questionnaire also included socio-demographic items and other questions to exhibit a profile of suicide tendency for each individual. RESULTS: Our results showed that the stress levels and well-being are comparable according to gender. Specifically the results showed that lack of sleep combined with a low score to McNair test strongly affects the suicidal tendency, while score of memory and attention decreased. CONCLUSIONS: The suicidal behaviour is closely linked with sleep parameters which decreased accordingly, and the family's history of medication and suicidal behaviour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".