Exploring ageism in medical education: A scoping review of the educational factors affecting the attitudes of medical students and junior doctors towards older inpatients
Bibliographic record
Abstract
OBJECTIVES: Ageism is a significant issue in health care and exists among medical learners in the teaching hospital setting. To highlight potential strategies and interventions that may address ageism among medical students and junior doctors, we aimed to explore and understand the educational factors within hospital training environments that influence the attitudes of medical students and junior doctors towards older inpatients. METHODS: A scoping review of academic peer-reviewed English literature was conducted using seven academic databases. The first 100 results of an advanced Google Scholar search were also screened. One researcher screened identified articles and extracted relevant data from those that were included. Two researchers analysed the data using a modified content analysis. RESULTS: A total of 18 articles were included for review, published between 1982 and 2023, mostly from English-speaking countries. The articles described factors contributing to both positive and negative attitudes of medical students and junior doctors towards older inpatients. Four themes regarding these attitudes were identified: (1) the intersectionality of patient age, comorbidity and complex care needs; (2) the hospital environment; (3) clinical interactions; and (4) training in and exposure to Geriatric Medicine. CONCLUSIONS: There are several educational factors in the hospital training environment that affect the attitudes of medical students and junior doctors towards older inpatients. Exposure to and training in Geriatric Medicine may promote more positive attitudes towards older inpatients, but further research is required to determine whether this is of clinical significance.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".