SOCIO-PSYCHOLOGICAL ASPECTS "HAPPY OLD AGE" AND OPPORTUNITIES OF SOCIAL SERVICES IN ITS PROVISION
Bibliographic record
Abstract
In this article the authors examine the anthropological, physiological, socio-psychological, medical background happy old age; analyze the data of a number of domestic and foreign experimental work to identify factors, conditions that ensure comfortable livelihoods of the elderly person. Special attention is paid to the role of the sociogerontological competence of managers of the sanatorium and resort sphere in the creation of socio-cultural gerontology centre environment to ensure a happy old age customers, as well as the traditions of the Kuban Cossacks in relation to older people. A separate section of the article is devoted to the development of the Institute of foster families for older people in the Krasnodar region. Significant place in the article devoted to the analysis of various areas of professional activity of specialists of social service agencies within the community to enhance the revitalization of the manifestation of older people in different types of cultural and educational activities. The article reveals the essence and content of the concept of "socio-gerontological competence of the specialist institution of social service of the population". The authors examine the nature and structural components of the socio-cultural environment gerontology centre (pension) sanatorium profile, which is a special kind of communicative space of the Board, determining its existence and prospects of development as a specialized Agency for the elderly.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".