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Record W2999379569 · doi:10.1177/0022146519887475

Newcomers and Old Timers: An Erroneous Assumption in Mental Health Services Research

2019· article· en· W2999379569 on OpenAlexaff
Carol S. Aneshensel, Jenna van Draanen, Helene Riess, Alice P. Villatoro

Bibliographic record

VenueJournal of Health and Social Behavior · 2019
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental Health
KeywordsPsychiatric epidemiologyMental healthEpidemiologyEthnic groupPremisePsychologyPsychiatryHomogeneity (statistics)Clinical psychologyMedicineStatisticsSociology

Abstract

fetched live from OpenAlex

Based on the premise that treatment changes people in ways that are consequential for subsequent treatment-seeking, we question the validity of an unrecognized and apparently inadvertent assumption in mental health services research conducted within a psychiatric epidemiology paradigm. This homogeneity assumption statistically constrains the effects of potential determinants of recent treatment to be identical for former patients and previously untreated persons by omitting treatment history or modeling only main effects. We test this assumption with data from the 2001–2003 Collaborative Psychiatric Epidemiology Surveys; the weighted pooled sample is representative of noninstitutionalized U.S. adults (18+; analytic n = 19,227). Contrary to the homogeneity assumption, some associations with recent treatment are conditional on past treatment, including psychiatric disorder and race-ethnicity—measures of need and treatment disparities, respectively. We conclude that the widespread application of the homogeneity assumption probably masks differences in the determinants of recent use between previously untreated persons and former patients.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.102
GPT teacher head0.510
Teacher spread0.408 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2019
Admission routes1
Has abstractyes

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