Awakening Canadians to ageism: a study protocol
Bibliographic record
Abstract
BACKGROUND: Making fun of growing older is considered socially acceptable, yet ageist humour reinforces negative stereotypes that growing old is linked with physical and mental deterioration, dependence, and less social value. Such stereotypes and discrimination affect the wellbeing of older people, the largest demographic of Canadians. While ageism extends throughout professions and social institutions, we expect nurses-the largest and most trusted group of healthcare professionals-to provide non-ageist care to older people. Unfortunately, nurses working with older people often embrace ageist beliefs and nursing education programs do not address sufficient anti-ageism content despite gerontological nursing standards and competencies. METHODS: To raise awareness of ageism in Canada, this quasi-experimental study will be supported by partnerships between older Canadians, advocacy organizations, and academic gerontological experts which will serve as an advisory group. The study, guided by social learning theory, will unfold in two parts. In Phase 1, we will use student nurses as a test case to determine if negative stereotypes and ageist perceptions can be addressed through three innovative e-learning activities. The activities employ gamification, videos, and simulations to: (1) provide accurate general information about older people, (2) model management of responsive behaviours in older people with cognitive impairment, and (3) dispel negative stereotypes about older people as dependent and incontinent. In Phase 2, the test case findings will be shared with the advisory group to develop a range of knowledge mobilization strategies to dispel ageism among healthcare professionals and the public. We will implement key short term strategies. DISCUSSION: Findings will generate knowledge on the effectiveness of the e-learning activities in improving student nurses' perceptions about older people. The e-learning learning activities will help student nurses acquire much-needed gerontological knowledge and skills. The strength of this project is in its plan to engage a wide array of stakeholders who will mobilize the phase I findings and advocate for positive perspectives and accurate knowledge about aging-older Canadians, partner organizations (Canadian Gerontological Nurses Association, CanAge, AgeWell), and gerontological experts.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".