Bibliographic record
Abstract
There is a code of silence regarding addicted doctors in medicine. While the doctor is minimizing or denying the problem, often her or his co-workers look the other way. Colleagues may be concerned but hold back from “denouncing” one of their own. Yet, ethical and legal issues are real. Patient care may be compromised. This presentation will engage listeners by asking several reflective questions. The 4 C’s of addiction will be reviewed. Signs of addiction will be enumerated. Why doctors become entangled in substances will be examined. Is addiction different from burnout? If so, how? The adverse consequences of addiction will be reviewed. How can compassion be offered for a problem that triggers blame and shame?Impaired doctors are usually referred to Physician Health Programs. What do they offer? Can the Buddhist view of addiction contribute to Western therapies? Addiction recovery will be examined through a mindfulness lens.This, however, still puts the onus on the individual who struggles with addiction. What about the medical culture may contribute to the problem? Can this be changed? If so, how? Addicted doctors are not alone, and the problem is more than personal. Rather than simply review the literature, this presentation will engage the audience so that the taboo of addiction can be tackled. It is intended to break the silence such that upon return to work, participants may notice a colleague who shows signs of addiction and then open their hearts to offer support.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".