Bibliographic record
Abstract
In the beginning of November, we had the wonderful opportunity to participate in a reunion of some 360 colleagues, plus 100 virtual attendants, in Saskatoon, following 2 years of enforced pandemic separation. While communications by Zoom provide new avenues for accessibility, observing the cheerful reunions in the corridors, the insights acquired from a dialogue with speakers and planning groups, and yes, even the exchange of gossip, was further evidence of our need for connectivity. Congratulations to the 2022 CSAM Conference Organizers. This issue includes 5 articles. We introduce them in the context of the information gained from the Conference. Spurred by the opioid crisis, the Journal receives regular submissions on opioid management, also a top topic of presentations. In this issue, 3 articles on this topic: first, a national online survey of representatives of Withdrawal Management (WM) programs. As anticipated, Rush et al1 report a relative lack of capacity in Opioid Agonist delivery as well as a need to standardize clinical guidelines. Second, the evidence for the transition from methadone to buprenorphine is so far based on case reports describing alternative methods. In the second article, Costa et al2 present the case of an individual with polysubstance use and stimulant-induced psychosis transitioning from methadone to eventually buprenorphine extended-release injection. Throughout, this focus on treatment retention is highlighted. Overall, this transition strategy based on creative case reports is in dire need of randomized control trials. The good news is that such trials are now registered in Canada3 and also in the United States. The third is a commentary by Dr Kleinman,4 who proposes additional practice guidelines to add to the developing national set. The involuntary hospital admissions of people with severe substance use disorders (SUD) remains a subject of controversy. Di Paola et al5 report on 3 cases, with advice as to how to preserve rapport and secure positive outcomes. Gooding et al6 compare couple therapy treatment seeking among people with SUDs versus gambling disorders from online data in a national survey. Conclusions are that treatment seeking in SUDs is mostly related to a more prominent comorbidity profile, while for gamblers, treatment seeking is more related to a greater addiction severity. Returning back to this year’s conference, a strong feature was its emphasis on social policies. If you have not yet heard of the Overton Window7 as a yardstick of political viability and social acceptance, may I recommend you “Google” it. This issue is to be published in March 2023. Hopefully, 2023 will turn out to be a healing and forward-looking year.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.413 | 0.305 |
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".