Is it time to get some SHUT-i?
Bibliographic record
Abstract
There are four central things that are well known about insomnia which together highlight it as a serious public health concern: (I) it is a significant issue - over a third of the population will experience an acute episode (less than three months in duration) of insomnia every year (1) and between 10–20% of the population will report chronic insomnia (Insomnia Disorder) at any point in time (2); (II) once chronic, it is a persistent disorder with low natural remission rates and high recurrence rates (3,4), (III) it is costly both directly (in terms of healthcare costs) and indirectly (e.g., lost productivity and performance, accidents) (5) and (IV) it is a significant risk factor for the development and/or worsening of many physical or psychiatric disorders (6). Fortunately, as our understanding of insomnia has increased so has our armoury of management strategies. Most notably, at least from a non-pharmacological perspective, has been the introduction of a series of techniques aimed to increase the biological drive to sleep, stabilise the circadian rhythm and break negative, whilst reinforcing positive, associations between the bed/bedroom and sleep (addressing the behavioural aspects of insomnia) and help manage sleep related preoccupation, worry and anxiety, and address dysfunctional attitudes and beliefs about sleep and unwanted nocturnal ruminations (addressing the cognitive aspects of insomnia). Over time these techniques have been packaged together, under an umbrella term, of what is now considered Cognitive Behavioural Therapy for Insomnia (CBT-I).
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.039 | 0.020 |
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".