‘I just need an opiate refill to get me through the weekend’
Bibliographic record
Abstract
In this article, we discuss the ethical dimensions for the prescribing behaviours of opioids for a chronic pain patient, a scenario commonly witnessed by many physicians. The opioid epidemic in the USA and Canada is well known, existing since the late 1990s, and individuals are suffering and dying as a result of the easy availability of prescription opioids. More recently, this problem has been seen outside of North America affecting individuals at similar rates in Australia and Europe. We argue that physicians are also confronted with an ethical crisis where a capitalist-consumerist society is contributing to this opioid crisis in which societal, legal and business interests push physicians to overprescribe opioids. Individual physicians often find themselves unequipped and unsupported in attempts to curb the prescribing of opioid medications and balance competing goals of alleviating pain against the judicious use of pain medications. Physicians, individually and as a community, must reclaim the ethical mantle of our profession, through a more nuanced understanding of autonomy and beneficence. Furthermore, physicians and the medical community at large have a fiduciary duty to patients and society to play a more active role in curbing the widespread distribution of opioids in our communities.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".