STAVOVI MEDICINSKIH SESTARA PSIHIJATRIJSKE BOLNICE RAB PREMA STARIJIM OSOBAMA
Bibliographic record
Abstract
In a society where elderly comprise almost a quarter of population having positive attitudes towards elderly and correct information about aging and change that comes with it is very important, especially for health workers. The aim of this paper was to assess the knowledge and attitudes of the nurses working in Psychiatric hospital Rab towards aging and the elderly. The data was collected using an anonymous questionnaire and a knowledge quiz. The research showed that respondents have mostly positive attitudes towards elderly, a correlation between respondents age and their assessment of the lowest chronological age they consider to be elderly was determined and a statistically significant difference was found in between respondents answers, considering their sex and age in one question, and their experience in working with elderly patients in another. Over 47% of respondents answered correctly to 75% or more questions in the administered knowledge quiz, while the lowest correct score was five, or just 42% of the questions. Further education of the personel could emphasise the importance of preventive measures, everyday physical activity a dropping bad habits as key factors in successful ageing.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".