A Call to Action: Arming Nurses With Political Knowledge to Advocate for Older People
Bibliographic record
Abstract
INTRODUCTION: For decades, healthcare systems have enacted cost containment, which has led to nurses working within increasingly thinly resourced healthcare services influenced by political ideologies and often just trying to get through their shift. They may not have enough energy, support or background knowledge about what is behind the challenges they face or how they could effectively advocate for older people. METHODS: In this discussion paper, we provide an overview of three underlying political ideologies; discuss how these ideologies influence health and social care among western countries, examine how workplaces influenced by political ideology (e.g., resource allocation) can perpetuate ageism in healthcare decisions; explore advocacy and activism in nursing and suggest opportunities for nurses to disrupt negative practices. RESULTS: We found common challenges among countries with the three ideologies-increasing older people, the need to spend money efficiently and older people's desire to age-in-place. Thus, there is critical importance of health and social community supports and informal caregivers. Although not perfect, a social democratic ideology appears to be more likely than the other ideologies to work towards valuing people of all ages, with greater potential for equitable access and to support people throughout their lifespan. CONCLUSIONS: Nurses have an important role to play in suggesting improvements in health and social care. Nurses that work together through professional gerontological associations could be proactive in informing the public that their current political ideology could be perpetuating ageism in health and social care systems. IMPLICATIONS FOR PRACTICE: Nurses need to educate themselves about the political ideology in their country and critically examine how it influences the health and social care of older people. Also necessary that nurses take an advocacy/activist role for older people's health and social care needs.
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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 | 0.066 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".