Assesment of scientists’ lifestyle and risk factors affecting their professional efficiency
Bibliographic record
Abstract
People involved in scientific research should keep their cognitive status high since this is necessary for preserving their intellectual potential and maintaining their work efficiency. Given that, it seems important to determine what impacts scientific work might have on mental health, to estimate potential disorders and to develop a strategy aimed at preventing cognitive impairments. Our research goals were to perform screening assessment of executive functions, to examine signs of premature ageing and to explore behavioral and social risk factors among Russian researchers. We accomplished a cross-sectional study with 213 researchers employed by state scientific institutions in Moscow participating in it; they were 116 women and 97 men aged from 23 to 78 years (their average age was 45.48 ± 15.33 years). As a result, we established that risk factors causing a decline in professional efficiency were rather frequent among the participants. Probable cognitive disorders were detected in 9.85 % of them and we should note that these disorders were not age-related. We detected signs of senile asthenia in 3.28 % of the participants and senile depression in 2.34 %. Two thirds of the participants had subclinical depression (74.6 %). Only one fifth of the respondents (19.71 %, n = 42) did not have any cognitive impairments, asthenic syndrome, or depression. A quarter of the researchers (25.34 %) were not sufficiently committed to healthy lifestyle. Low physical activity established for 79.3 % of the respondents was the major risk factor; among others, we can mention irrational nutrition, primarily among those researchers who worked with students; poor stress management skills among physicians who combined clinical practice with science; difficulties in interpersonal relationships among people who dealt solely with research. It is necessary to implement corporate programs aimed at prevention and rehabilitations for researchers in order to preserve their scientific activity and professional efficiency as well as to extend their professional longevity
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".