MétaCan
Menu
Back to cohort
Record W2140543061 · doi:10.1002/hup.452

Are there gender differences in the prescribing of hypnotic medications for insomnia?

2002· article· en· W2140543061 on OpenAlexaffabout
Keith Brownlee, Gerald M. Devins, M. J. Flanigan, Jonathan A.E. Fleming, Rachel Morehouse, Adam Moscovitch, Jacques Plamondon, Lawrence W. Reinish, Colin M. Shapiro

Bibliographic record

VenueHuman Psychopharmacology Clinical and Experimental · 2002
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité LavalUniversity of CalgarySaint John Regional HospitalUniversity of British Columbia HospitalUniversity Health NetworkUniversity of TorontoCentre for Addiction and Mental HealthToronto Western HospitalLakehead University
Fundersnot available
KeywordsZopicloneInsomniaPharmacyMedicineMedical prescriptionPsychiatryHypnoticMarital statusFamily medicinePopulationPharmacology

Abstract

fetched live from OpenAlex

Gender differences in the prescribing patterns of general classes of medications for insomnia were examined. The classes of medications included: zopiclone, antidepressants, benzodiazepines, antihistamines and no medication. The sample comprised a sub-set of respondents from 2620 questionnaires of the Canadian Multicentre Sleep Database. Respondents for this database were contacted through physicians, announcements in the media and local pharmacies. The results indicated that gender alone was not associated with differential prescribing for insomnia, nor was gender associated with patterns of medication use such as frequency of taking medication, length of use, taking more or less medication than prescribed or attempts to stop taking medication. Demographic factors were included in the analysis and age and marital status were associated with different prescribing patterns for men and women with insomnia. It is possible that physicians refer to stereotypic expectations when prescribing hypnotics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.175
GPT teacher head0.447
Teacher spread0.272 · 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 teacher head, not a consensus.

Study designObservational
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

Citations11
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueHuman Psychopharmacology Clinical and ExperimentalSame topicSleep and related disordersFrench-language works237,207