Insomnia Management in Primary Care: Outcomes from a Canadian National Survey Reveal Challenges and Opportunities to Improve Clinical Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it