Preparedness of health care providers for motivaton of the population for leading a healthy lifestyle
Bibliographic record
Abstract
Purpose. To assess the commitment to a healthy lifestyle of persons involved in the formation of health conservation (doctors, teachers). Materials and m e t h o d s . A cross-sectional study was conducted, which included 176 people (75 doctors, 101 teachers of higher medical educational institutions), employees of state budgetary institutions in Moscow. At the age of 45,48±15,33 years (from 23 to 78 years), among them 91 women, 85 men. Results. A quarter of the respondents from the group led an insufficiently healthy lifestyle (25,34%); 4% of doctors and 0,9 teachers observed “poorly” the principles of a healthy lifestyle; The main problem was low physical activity, identified in 79,3% of the respondents (95,6% of doctors, 67,56% of teachers), low responsibility for health (9,9%) and poor nutrition among teachers (3,9%), low stress management skills among doctors (5,3%). Conclusions. There is a lack of adherence to the principles of a healthy lifestyle among participants in health preservation. Insufficient commitment of doctors and teachers to adherence to the principles of a healthy lifestyle plays a negative role in the formation of health-saving behavior among the population of the Russian Federation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".