INSTITUTIONS OF PROFESSIONAL ASSISTANCE TO FAMILIES WITH CHILDREN IN THE ESTIMATES OF THE VOLOGDA OBLAST POPULATION
Bibliographic record
Abstract
The purpose of this article is the analysis of the attitude of the Vologda Oblast population with children toward the institutions of professional assistance to families with children, namely, to the services of psychologists and nannies. The information base was the results of the next stage of the sociological monitoring of the reproductive potential of the population of the Vologda Oblast, conducted by the Vologda Research Center of the Russian Academy of Sciences in 2021. The study found that despite the low prevalence of practices of applying for professional services of family psychologists and nannies among the population of the Vologda Oblast, the estimates of the assistance provided were quite high, which indicates the effectiveness of this kind of assistance for families with children. More than a quarter of respondents (27 %) expressed their willingness to seek the help of a psychologist, and almost every fifth respondent (19 %) expressed their willingness to seek the services of a professional nanny. The main reasons for refusing professional help are the unwillingness to take problems out of the family and the attitude to solve them independently. There are noticeable differences in the experience of applying for professional help from psychologists and nannies, in their assessments, readiness to receive such services and the reasons for refusing them, depending on the gender, age, marital status of respondents, their satisfaction with marriage, the number of children in their families, including the presence of minor children.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".