The Curse of Old Age, an Evaluation of Current Medical Discrimination Based on Age and Future Directions in Resolving Ageism
Bibliographic record
Abstract
Ageism refers to the discrimination or bias held against people because of their age regardless of context.This phenomenon has been especially rampant in the medical context.Older patients past the age of 50 tend to receive less attention and care during their time at the hospital, both during diagnosis and prognosis.Aside from some general biased ideas, one of the most common factors influencing the degree of ageism experienced by people in a specific region is culture.Some cultures have high reverence for people older in age, while other cultures frantically praise youth and spurn the elderly.This has expanded beyond the medical context during Covid times, onto social media and into daily life, which have been damaging older adults' mental and physical health.The mind and body are connected, which means an unhealthy mental state can have an effect on the person's body.For example, anxiety can increase the chances of depression and insomnia.This anxiety is the result of both exterior and interior factors.In order to untangle this complicated web of culture, anxiety, discrimination, and self-awareness, this paper will outline and evaluate current circumstances of ageism, collect data from the Port Hope community in Ontario, Canada to investigate further, and eventually propose an evidence-based strategy and future directions.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".