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Record W2025501801 · doi:10.1002/mpr.296

Has ‘lifetime prevalence’ reached the end of its life? An examination of the concept

2009· article· en· W2025501801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Methods in Psychiatric Research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of CalgaryBaycrest HospitalMcMaster UniversityUniversity of Toronto
FundersNational Institute on Drug Abuse
KeywordsDepression (economics)EpidemiologyCohortDemographyAnxietyPrevalenceCohort effectMedicineCohort studyMortality ratePsychiatryGerontology

Abstract

fetched live from OpenAlex

Many cross-sectional surveys in psychiatric epidemiology report estimates of lifetime prevalence, and the results consistently show a declining trend with age for such disorders as depression and anxiety. In a closed cohort with no mortality, lifetime prevalence should increase or remain constant with age. For mortality to account for declining lifetime prevalence, mortality rates in those with a disorder must exceed those without a disorder by a sufficient extent that more cases would be removed from the prevalence pool than are added by new cases, and this is unlikely to occur across most of the age range. We argue that the decline in lifetime prevalence with age cannot be explained by period or cohort effects or be due to a survivor effect, and are likely due to a variety of other factors, such as study design, forgetting, or reframing. Further, because lifetime prevalence is insensitive to changes in treatment effectiveness or demand for services, it is a parameter that should be dropped from the lexicon of psychiatric epidemiology.

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.

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)0.027
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.208
GPT teacher head0.572
Teacher spread0.364 · 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