Technical Difficulties: Teaching Critical Philosophical Orientations toward Technology
Bibliographic record
Abstract
Issue: Technological innovation is accelerating, creating less time to reflect on the impact new technologies will have on the medical profession. Modern technologies are becoming increasingly embedded in routine medical practice with far-reaching impacts on the patient-physician relationship and the very essence of the health professions. These impacts are often difficult to predict and can create unintended consequences for medical education. This article is driven by a main question: How do we prepare trainees to critically assess technologies that we cannot foresee and effectively use technology to support equitable and compassionate care? Evidence: We translate insights from the philosophy of technology into a proposal for integrating critical technical consciousness in medical curricula. We identify three areas required to develop critical consciousness with regard to emerging technologies. The first area is technical literacy, which involves not just knowing how to use technology, but also understanding its limitations and appropriate contexts for use. The second area is the ability to assess the social impact of technology. This practice requires understanding that while technification creates new possibilities it can also have adverse, unintended consequences. The third area is critical reflection on the relationship between ‘the human’ and ‘the technical’ as it relates to the values of the medical profession and professional identity formation. Human and technology are two sides of the same coin; therefore, thinking critically about technology also forces us to think about what we consider ‘the human side of medicine’. Implications: Critical technical consciousness can be fostered through an educational program underpinned by the recognition that, although technological innovation can create new possibilities for healing, technology is never neutral. Rather, it is imperative to emphasize that technology is interwoven with the social fabric that is essential to healing. Like medication, technology can be both potion and poison.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".