Ageism, gerontological nursing and healthcare contexts
Bibliographic record
Abstract
Ageism, gerontological nursing and healthcare contexts Professors Kathleen Hunter and Sherry Dahlke from the University of Alberta’s Faculty of Nursing explain why gerontological nursing education is key to addressing the unconscious negative stereotypes about ageing and improving care for older adults. Ageism is worldwide and is apparent in healthcare professions and health systems. (1) This is partly due to healthcare systems, particularly hospitals, that are institutionally ageist because they are designed for younger people with one acute condition rather than the majority of users who are older adults with chronic and acute conditions. (2) Moreover, healthcare professionals, of which nurses are the largest group that interact with patients, may be unconscious about their negative biases towards older people. Nurses’ unconscious biases are often demonstrated by overaccommodating older patients due to an underlying belief that older people are less capable. (3) Nurses may feel pressured to engage in overaccommodation to save time because they are working in hospitals where they experience time constraints due to short staffing, lack of material resources, and hospital cultures that focus on medical acuity. (4)
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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 | 0.004 | 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 | 0.007 | 0.032 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".