Insomnia Management in Primary Care: Outcomes from a Canadian National Survey Reveal Challenges and Opportunities to Improve Clinical Practice
Bibliographic record
Abstract
Insomnia is prevalent yet remains underrecognized and inconsistently treated in Canadian primary care. Significant learning and knowledge gaps exist for Canadian Primary Care Physicians (PCPs) managing patients with insomnia. Consequently, Canadian PCPs were invited to participate in a national Needs Assessment survey to provide real-world insights into the management of insomnia and to identify current gaps in clinical care of insomnia. A Steering Committee comprising Canadian psychiatrists and PCPs, with strong expertise in insomnia, collaborated on a national survey on the management of insomnia and a subsequent article exploring survey results. The Collaborative CME and Research Network (CCRN) and the article authors validated the content and conducted factor analysis for construct validity to assess the survey's validity and reliability. Data were analyzed using descriptive statistics to summarize and identify trends. CCRN ensured appropriate regional representation in the survey roll-out and subsequent collection of responses. Survey findings revealed limitations in training, skills, and knowledge regarding insomnia management. Critical knowledge and learning gaps identified through the survey underscored the need for training and targeted Continuing Medical Education (CME) to help Canadian Healthcare Providers (HCPs), especially PCPs, understand better the complexities of insomnia. Barriers include reluctance to recommend cognitive behavioural therapy in insomnia (CBT-I) and limited awareness of the orexin pathway’s role in the sleep/wake cycle, as well as therapies specifically indicated for insomnia. This article highlights the need to address these barriers to help HCPs better support their patients with insomnia and alleviate the burden on the healthcare system.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".