Delphi consensus recommendations for the management of chronic insomnia in Canada
Bibliographic record
Abstract
The lack of current Canadian practice guidelines for the management of insomnia poses a challenge for healthcare providers (HCP) in selecting the appropriate treatment options. This study aimed to establish expert consensus recommendations for the management of chronic insomnia in Canada. Sixteen multidisciplinary experts in sleep medicine and insomnia across Canada developed consensus recommendations based on their knowledge of the literature and their practical experience. The consensus recommendations were developed through a Delphi method. Consensus was reached if at least 75 % of the voting participants “agreed” or “strongly agreed” with the corresponding statements. The quality of supporting evidence was rated using a GRADE rating system. Among 37 recommendations that reached consensus for the management of chronic insomnia, the experts recommend and agree that. These consensus recommendations highlight the need to increase awareness, capacity for, and access to CBT-I; integrate newly approved pharmacotherapy; reduce both self-medication and medications with limited evidence or low risk/benefit ratio. •Despite the availability of different therapies for managing chronic insomnia, more evidence-based guidance is needed to help healthcare providers select the most suitable interventions, especially with the recent approval of new treatments. • Based on the review of evidence, experts reached a consensus on recommendations that (1) reinforce the role of Cognitive-Behavioural Therapy for Insomnia as a first-line treatment; (2) assist healthcare providers in effectively integrating the newly approved medication therapies in their treatment decisions; and (3) emphasize the importance of exercising caution when prescribing medications for which the evidence is limited or the risk/benefit low.
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 | 0.218 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".