Addressing ageism in healthcare through gerontological nursing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Addressing ageism in healthcare through gerontological nursing Sherry Dahlke, Associate Professor at the Faculty of Nursing, University of Alberta, discusses the impact of ageism in healthcare and why gerontological nursing education is vital for improving awareness and patient care. The World Health Organization’s (WHO, 2021) global report on ageism reports that one in every two people is ageist towards older people. Ageism includes stereotypes about aging and older adults (beliefs), prejudice (feelings) and/or discrimination through actions (WHO, 2021). Ageism can occur between people, be institutionalized, and/or self-directed. For example, ageism occurs in healthcare when older people who often have complex health and social needs are expected to fit into systems designed for younger people with one health concern (Kojima, 2018), resulting in adverse health outcomes (Chang et al., 2020). Exposure to negative stereotypes of aging can lead people to internalize negative beliefs of inevitable decline, resulting in them experiencing adverse effects of ageism as they age (Levy, 2009; Steward, 2022).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it