Spinning the lens on physician power: narratives of humanism and healing
Bibliographic record
Abstract
Divisive, disabling and dangerous power has featured heavily in health professions literature, social media and medical education. Negative accounts of the wielding of power have discoloured the lens through which the public sees medicine and distorted the view of a profession long associated with healing, humanism and heart. What has been buried in the midst of this discourse are positive accounts of power where the yielding of power is encouraging, empathetic and empowering. This article offers three personal vignettes illustrating the ability of power to positively affect lives in the practice of medicine, for patients and doctors alike. More of these stories are needed to uplift and rebalance the conversation on physician power and how it can be used for good. It is necessary to provide a narrative framework of what it looks like to be a healer and a humanistic doctor to satisfy the general public through a commitment to cultivate multidimensional future healthcare providers.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.078 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| 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".