Bibliographic record
Abstract
Slumber at scale: a digital solution for a tiresome problemWe are grateful to Dr Judith Davidson and her team from Canada for their focus on this highly prevalent condition and for highlighting the effectiveness of cognitive behavioural therapy (CBT) for insomnia within primary care. 1 Indeed, CBT is the treatment of choice according to clinical guidelines.A significant challenge however is how to deliver effective treatment at scale.Certainly within the NHS, where some 12 million prescriptions for sleeping pills are still written annually, it is difficult to imagine there being an adequate supply of clinical psychologists or trained therapists to deliver this CBT.Both logistical and financial barriers suggest that we must look to a more pragmatic and scalable solution.Digital CBT directly addresses this impasse, offering a demonstrably effective, accessible solution that is also readily scalable and cost-effective.The evidence is strong for equivalence in treatment outcomes between this and more traditional modes of delivery, yet the ability to immediately apply it at population scale is a unique benefit.In addition, the positive outcomes permeate through other health domains with significant improvements shown in mental health and wellbeing.2,3 Brief clinical tools such as the two-item Sleep Condition Indicator (SCI-02) 4 are also now available to appraise insomnia in general practice.They are well validated and memorable enough to screen for the majority of cases.GPs have been calling for a solution to the escalating hypnotic prescribing problem and digital CBT can provide it.Sleepio (https:// www.sleepio.com/) is one such programme that is referenced as clinically effective in international clinical guidelines, has been subject to NICE MIB briefing, and, through NHS innovation funding, it is being rolled out across London and the Thames Valley.Minimal training is required for this type of automated digital medicine and ways of delivering digital therapies to patients in primary care are being developed.Further work is needed to understand exactly how such solutions can be recommended or prescribed by primary care clinicians; however, the potential for evidence-based digital CBT to satisfy clinical demand for an effective insomnia treatment is compelling.A radical, population-scale approach to this most ubiquitous of problems is long overdue.
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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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.026 | 0.027 |
| Insufficient payload (model declined to judge) | 0.077 | 0.042 |
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".