Positive attitudes towards older adults: characteristics of prospective partners in care
Bibliographic record
Abstract
With the increase in the aging population it is becoming all the more important to \ndetermine who has positive attitudes towards older adults in order to identify those who are best \nsuited to work in geriatrics. The purpose of the current study was to determine which personal \ncharacteristics are indicative of positive attitudes towards older adults by using the Cattell 16 \nPersonality Factor Questionnaire and the Kogan’s Attitudes Towards Old People scale. \nCharacteristics that were examined included ethnicity, age, gender, and level of education and \npersonality. Caucasian and Indigenous participants were recruited around Sudbury Ontario, \nranging from 18 to 50 years of age. The results suggest that gender and personality factors \nwarmth, reasoning, vigilance, privateness and openness to change, are predictive of positive \nattitudes towards older adults. These results have implications for identifying individuals who \nare best suited to work in geriatrics and possibly encouraging those to join the field.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".