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Record W4406675811 · doi:10.56367/oag-045-11791

Solutions for better sleep

2025· article· en· W4406675811 on OpenAlexafffund
Lianne Tomfohr‐Madsen

Bibliographic record

VenueOpen Access Government · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsSleep (system call)Computer scienceEnvironmental scienceOperating system

Abstract

fetched live from OpenAlex

Solutions for better sleep Dr Lianne Tomfohr-Madsen highlights sleep health equity research to inform best practices and policies to improve the sleep of all Canadians. We spend about a third of our lives asleep. Sleep is modifiable, and better sleep contributes to personal and community wellbeing, making optimizing sleep health an urgent public health priority. The demands of a Western lifestyle (urgency, productivity), economic stressors, discrimination, loneliness, and global crises all contribute to poor sleep health, particularly for people who are the most socially and economically vulnerable. (1) Focusing on prevention and intervention through evidence-informed approaches is essential to enhance health outcomes and quality of life.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0060.008
Open science0.0020.007
Research integrity0.0100.021
Insufficient payload (model declined to judge)0.0610.021

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.

Opus teacher head0.048
GPT teacher head0.401
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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