Bibliographic record
Abstract
Entitlement is a problem in medical education that has received relatively little attention. First, it is felt by educators, who often feel pressured to conform curricula and evaluations to satisfy learner demands and administrative pressures lest their careers are penalized. Second, entitlement affects the medical system, as entitled physicians are less empathetic and focus more on personal goods rather than patient needs. This shifts the humanistic basis for medicine. Thirdly, entitlement is problematic for learners, as constant accommodations ironically undermine self-reliance and adaptability. Constantly meeting these demands can diminish gratitude and overall happiness. To address this issue, learners must acknowledge the problem and seek remedies to it themselves, as top-down interventions will likely be rebuffed. Rather than focusing solely on the learning environment, solutions should also empower learners to engage their environment in effective and productive ways. This should include correcting cognitive distortions that lead learners to expect administrative interventions in all circumstances perceived as harmful. Other solutions include practicing gratitude and developing work friendships. While certainly not all learners are entitled and some environmental amendments should be made, learners and educators must realize that entitlement is shaping medical culture and collectively take steps to mitigate its negative effects.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".