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Record W2077586916 · doi:10.1037/a0018776

Emotion regulation strategies and goals as predictors of older mothers’ and adult daughters’ helping-related subjective well-being.

2010· article· en· W2077586916 on OpenAlexafffund
Tanya S. Martini, Michael A. Busseri

Bibliographic record

VenuePsychology and Aging · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAffect (linguistics)Developmental psychologyContext (archaeology)DaughterSocial psychologyAffect regulationLife satisfactionAttachment theory

Abstract

fetched live from OpenAlex

We examined emotion regulation (ER) in intergenerational helping relationships involving 77 older mother-adult daughter dyads. Participants' ER strategies (passive, proactive) and ER goals (self-oriented, other oriented) were considered as predictors of their own and their partners' satisfaction with, and their positive and negative affective reactions to, the helping relationship. For mothers and daughters, greater use of passive ER strategies predicted greater negative affect, lower satisfaction, and less positive affect for themselves, as well as partner reports of lower satisfaction and (for daughters only) greater negative affect. Mothers' and daughters' use of proactive strategies predicted lower negative affect for themselves, and daughters' use of proactive ER strategies predicted lower negative affect for their mothers. Mothers' and daughters' endorsement of other-oriented ER goals predicted greater satisfaction and positive affect for themselves. Results are considered in the context of the larger literature concerning intergenerational helping relationships.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.277
Teacher spread0.273 · 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

Citations38
Published2010
Admission routes2
Has abstractyes

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