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Record W4238158184 · doi:10.22215/etd/2017-12227

Getting Better with Age: Single-Nucleotide Polymorphisms in the relations Among Social Identity, Resilience, and Mental Health Among Retirement Community Residents

2017· dissertation· en· W4238158184 on OpenAlexaff
Olivia Pochopsky

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsMental healthPsychologyCoping (psychology)PsychosocialSingle-nucleotide polymorphismPsychological resilienceClinical psychologyDevelopmental psychologyPsychiatrySocial psychologyGeneticsGene

Abstract

fetched live from OpenAlex

Given the dynamic nature of mental health throughout the lifespan, this study examined associations among several psychosocial factors, single-nucleotide polymorphisms (SNPs) for brain-derived neurotrophic factor (BDNF), neuropeptide y (NPY), and oxytocin receptor (OXTR), and mental health among older (N=88) and younger adults (N=369).Older age was related to lower stress and depression, greater social identity and resilience, and various coping styles.Moreover, OXTR moderated the direct relationship between age and depression, as well as the mediating roles of identity and coping in that relation.OXTR also moderated the mediating role of coping in the relation between social identity and depression.However, age also moderated this relation both directly, and indirectly through the mediating role of resilience.These results suggest that identity, resilience, coping and OXTR may be protective to mental health, but that these factors may impact the mental health of older and younger adults differently.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.043
GPT teacher head0.365
Teacher spread0.322 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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