Mapping the insomnia patient journey in Europe and Canada
Bibliographic record
Abstract
Introduction: Insomnia affects daily functioning and overall health, and is thus associated with significant individual, societal, and economic burden. The experience of patients living with insomnia, their perception of the condition, and its impact on their quality of life is not well documented. The objective of this study was to map the patient journey in insomnia and identify unmet needs. Methods: Participants were individuals with insomnia, and healthcare professionals (HCPs) who treat insomnia, in the United Kingdom, France, Germany, Italy, and Canada. Qualitative interviews (50 patients, 70 HCPs) and a quantitative survey (700 patients, 723 HCPs) were conducted to inform the patient-journey mapping and obtain information on the emotions, perceptions, and experiences of patients and HCPs. Results: The patient journey comprises seven phases. The first defines the onset of insomnia symptoms. Phase 2 represents self-initiated behavior change to improve sleep (e.g., sleep hygiene, reducing caffeine, exercise). The next phase is characterized by use of over-the-counter (OTC) treatments, which generally fail to provide lasting relief. Phase 4 describes the first HCP consultation (occurring several months to several years after onset) and typically occurs at a crisis point for the patient; patients may be looking for an immediate solution (e.g., medication), which may not align with their HCP's recommendation. The following stage comprises sleep hygiene/behavioral changes (±OTC treatment) under HCP guidance for many patients, although offering prescription treatments without a sleep hygiene stage under supervision is more common in some countries. Phase 6 describes prescription medication initiation, where patients fluctuate between relief/hopefulness and a sense of failure, while HCPs try to balance the need to provide relief for the patient while maintaining best medical practice and minimizing adverse effects. The final phase (living with long-term insomnia) represents an indefinite period during which sleep issues remain unresolved for many patients, with most of them continuing to use prescription treatments for longer than indicated and creating their own variable, self-managed regimens combining multiple modalities. Conclusion: This patient journey analysis for insomnia revealed seven distinct phases, highlighting different touchpoints where insomnia management could be optimized.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".