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Record W4312104801 · doi:10.1093/geroni/igac059.478

CUMULATIVE DAILY DISCRIMINATION AS A RISK FACTOR FOR REDUCED PURPOSE

2022· article· en· W4312104801 on OpenAlexaff
Megan E. Wilson, Lydia Ong, Gabrielle N. Pfund, Anthony L. Burrow, Nancy L. Sin

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFeelingPsychologyAge discriminationDevelopmental psychologyGerontologyDemographyMedicineSocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract Past research shows that discriminatory experiences may reduce sense of purpose among older adults, though these associations are inconsistent across groups. Work is needed both to understand the nuanced role discrimination plays on individuals’ sense of purpose, whether it leads to feeling derailed from life goals, and if effects differ for younger versus older adults. The current study asked 354 American adults (age 19-74) to complete daily surveys over the course of two weeks, to examine whether experiencing discrimination during that two-week period led to a decline in purposefulness. Overall, findings suggest marked stability in sense of purpose during this short timeframe. However, greater daily discrimination predicted decreases in sense of purpose, and increases in derailment over the two weeks. While these associations were similar across age, older adults did report less discrimination at baseline. Findings will be discussed with a focus on successful aging among marginalized groups.

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)0.004
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.436
Teacher spread0.335 · 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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