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Record W4312103607 · doi:10.1093/geroni/igac059.1086

PSYCHOLOGICAL WELL-BEING IN OLDER ADULTS WITH TREATMENT-RESISTANT DEPRESSION

2022· article· en· W4312103607 on OpenAlexaboutno aff
Selmi Kallmi, Ann M. Steffen, Eric J. Lenze

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyDepression (economics)Affect (linguistics)Life satisfactionClinical psychologySocial supportGerontologyPsychiatryPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Abstract Geriatric psychiatry research has documented the importance of psychological well-being to older adults diagnosed with depression (Lenze et al., 2016). This presentation utilizes pre-treatment data from the Optimum Study of treatment-resistant depression in older adults recruited from five USA and Canadian metropolitan areas (N = 529). Social participation was measured with the PROMIS scale: Ability to Participate in Social Roles and Activities (Hahn et al., 2014). Positive affect and life satisfaction were assessed using scales from the Psychological Well-Being subdomain (Salsman et al., 2013) of the NIH Toolbox for Assessment of Neurological and Behavioral Function - Emotion Battery (NIHTB-EB). Along with associations between social participation and positive affect (r = .38, p < .01) and between social participation and life satisfaction (r = .26, p < .01), path analyses explored social participation as a mediator of the relationship between cognitive functioning (NIHTB-CB; Weintraub et al., 2013) and psychological well-being.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.356
Teacher spread0.332 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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