Bibliographic record
Abstract
Whether we realise it or not, we all hold stereotypes about older people.Whether we view them as cute, forgetful, bad with technology or unproductive, these stereotypes are harmful to older people and ourselves.What is more, ageism in healthcare environments impacts the quality of care that older people receive.At the University of Alberta in Canada, Dr Sherry Dahlke and Anndrea Vogt are exploring the impacts of ageism on older people and developing educational resources to teach student nurses how to best care for older patients. How can we combat ageism in society and healthcare?Gerontological nursing O ne of society's less-visible prejudices is ageism: discrimination against older people.Stereotypes of older people may at first glance appear relatively harmless, but can, in fact, have farreaching consequences."When society holds negative views of ageing, we are all affected, because we are all ageing," says Dr Sherry Dahlke, a nurse researcher at the University of Alberta.If we view older people as infirm, inactive and not involved in society, then when we grow old, we are more likely to accept these stereotypes about ourselves and limit our lives Talk like a ... gerontological nurse Acute illness -a treatable shortterm health condition Ageism -prejudice or discrimination due to a person's age Chronic illness -a long-term health condition that can usually be controlled, but not cured Dementia -a general term for conditions that impair a person's ability to think, remember or make decisions, such as Alzheimer's disease Gerontological -related to ageing and older people Palliative care -medical care that involves relieving a person's symptoms rather than curing the illness, often used near the end of a person's life Social learning theory -the idea that social behaviour is learnt by observing and mimicking the behaviour of others
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".