Bibliographic record
Abstract
Canada has an expansive, challenging geography with diverse demographics. The country is an industrialized and democratic nation situated at the northern end of the Americas. Canada provides universal healthcare to all residents through a singlepayer system administered by its provinces and territories. Data suggests common sleep disorders are present at similar rates in other industrialized nations, with the exception of a larger number of shift workers and arctic residents subject to circadian disruption. Canada has ‘punched above its weight’ in contributing to the field of sleep medicine, with numerous well-known pioneering specialists in areas ranging from pathophysiology and diagnostic development, to pharmacologic, therapeutic and device treatment. The practice of sleep medicine is provided by trained physicians in neurology, respirology, psychiatry, internal medicine, family practice, otolaryngology, pediatrics, as well as psychology and dentistry amongst other providers. Major challenges to Canadian sleep medicine include limited public healthcare funding, variable funding mechanisms across the nation’s jurisdictions, limited access to diagnostic and therapeutics, and conflicts-of-interest with business. Certain demographic groups are particularly at-risk, including socioeconomically challenged communities, indigenous populations, and other diverse minority groups. Canada’s characteristics and challenges provide it with substantial research opportunities and a chance to lead in such areas as epidemiology, sleep medicine genetics, ethnic and cultural aspects, circadian and shift work considerations, home polysomnography and post-COVID transitions to more virtual sleep medicine care.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
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".