The Ethos of Medicine in Postmodern America: Philosophical, Cultural and Social Considerations
Bibliographic record
Abstract
We need good data to practise good medicine. However, if a system becomes dominated by electronic health records (EHRs), computerized decision-making programs, and excessive guidelines and protocols, physicians can become pawns in “the silicon cage.” Arnold Eiser, professor of medicine and associate dean at Drexel University College of Medicine, is concerned about the erosion of both the patient-physician relationship and professionalism in the corporate world of American medicine. This postmodern world is characterized by what he calls “the three big C’s” of American medicine: consumerism, computerization, and corporatization. He notes that it is difficult to gain a broad perspective of the changes in health care when you are living through it. To provide an overview, he employs a wide-angle lens that includes postmodern philosophers, contemporary commentators, bioethicists, policy makers, and experiences from other countries (although this final aspect is relatively thin). Eiser begins by recounting the changes since 1970, when the health care system became more corporate. This business model increasingly viewed health care as marketable services and commodities, which was aided by challenges to the tradition of physician power and paternalism, coupled with social trends that favoured individualism, autonomy, and entitlements.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.109 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".