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Record W4387539791 · doi:10.1080/21642850.2023.2268697

A pilot cross-sectional investigation of chronic shame as a mediator of the relationship between subjective social status and self-rated health among middle-aged adults

2023· article· en· W4387539791 on OpenAlexafffund
Ellen McGarity‐Shipley, Eun‐Young Lee, Kyra E. Pyke

Bibliographic record

VenueHealth Psychology and Behavioral Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsResponse Biomedical (Canada)Queen's University
FundersQueen's University
KeywordsShameCross-sectional studyPsychologyMediatorClinical psychologySocial supportGerontologyDemographyMedicineSocial psychologyInternal medicine

Abstract

fetched live from OpenAlex

Subjective social status (SSS) is an important independent predictor of health outcomes, however, the pathways through which it affects health are poorly understood. Chronic shame has previously been suggested as a potential mechanism but this has never been investigated and the relationship between chronic shame and health is under-researched. The purpose of this pilot study was to explore whether chronic shame explains a significant portion of the association between SSS and self rated health (SRH). Two-hundred American adults aged 30-55 years were recruited via a crowd-sourcing platform and were asked to provide information on their SSS, level of chronic shame, and SRH. Chronic shame significantly mediated the relationship between SSS and SRH. This pilot study provides initial evidence that shame explains a significant portion of the relationship between subjective social status and self-rated health. These findings support the initiation of larger, longitudinal investigations into chronic shame as a mediator of the subjective social status and self-rated health relationship.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.215
GPT teacher head0.472
Teacher spread0.256 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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