Bibliographic record
Abstract
In this issue, Drs Morley‐Forster, Clark, Speechley and Moulin report on their survey conducted by Ipsos‐Reid in June 2001 (pages 189‐194). Only physicians who met the eligibility criteria of having written 20 or more prescriptions for moderate to severe pain in the preceding four weeks or having devoted 20% of their time to palliative care were eligible to participate. Sixty‐eight per cent of the respondents thought that moderate to severe chronic pain was not well managed in Canada. Despite this opinion, 23% of physicians in palliative care practice and 34% of primary care doctors stated that they would not use opioids to treat moderate to severe chronic noncancer pain even as a third‐line treatment after two previous medications had failed. One‐quarter to one‐third were concerned about the potential for addiction, and a smaller percentage reported concern about the potential for patient abuse and/or misuse, and side effects. Fear of a College audit resulting in the loss of their medical licence was cited by 10% of primary care physicians. When asked what obstacle hindered their use of strong opioid analgesics, an unexplained 10% of palliative care doctors and 14% of primary care doctors answered "nothing in particular".
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.024 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".