Bibliographic record
Abstract
Ageism in healthcare is a pervasive reality that leads to negative health outcomes for older adults. While it is often implicit, the COVID-19 pandemic threw explicit age discrimination in healthcare into sharp relief globally. In medicine, ageism translates into myriad forms of age discrimination that impact the provision of ethical care and range from 'micro' individual issues like paternalistic medicine or therapeutic nihilism to 'macro' system issues including barriers to timely and effective healthcare or exclusion from research trials. The culture of ageism in medicine can be unintentionally transmitted through role-modelling and the hidden curriculum. Strategies to combat ageism and provide ethical healthcare include intergenerational learning, educational programs, and strong leadership from organizations to enact policy and practice changes.
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.
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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.016 | 0.030 |
| 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".