Chronic pain and healthcare education in Canada: Bridging the divide
Bibliographic record
Abstract
Chronic pain and healthcare education in Canada: Bridging the divide Hansel Lui from the Michael G. DeGroote Institute for Pain Research and Care discusses inadequacies in healthcare education regarding chronic pain management in Canada and opportunities to close these gaps. In this article, Hansel Lui, part of our team at the Michael G DeGroote Institute for Pain Research and Care and the National Pain Centre, provides an additional perspective on ‘pain care in Canada’ – one of the underlying elements of the opioid crisis in Canada. This is the amount of training for pain care that is provided to healthcare professionals. Much has been said about the role of physician prescribing in the origins of the opioid crisis, especially in North America. It is the case that most prescriptions for opioids are for the treatment of pain, either acute or chronic. Chronic pain is a pervasive health issue in Canada, yet education on its management is underrepresented in healthcare curricula. Recognition that chronic pain is not a simple biological problem but often a complex biopsychosocial phenomenon has led to further recognition that optimal treatment requires an understanding of this complexity and the availability of appropriate interdisciplinary treatment. However, this degree of understanding has not yet transitioned from the field of pain experts into the general level of knowledge and awareness of the broad range of practicing healthcare professionals in Canada. In its 2014 ‘First Do No Harm’ [FDNH] document, (1) the Canadian Center on Substance Use and Addiction [CCSA] made the point that effectively addressing the opioid crisis required not only an understanding of addiction but also an understanding of pain, its origins, impact and appropriate treatment. Fifty percent of the recommendations in the document addressed issues related to pain, including the education of healthcare professionals. This article explores recent progress, including adopting IASP guidelines, innovative teaching methods, and opportunities to close gaps in pain education.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".