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Record W1844085676 · doi:10.1080/08870446.2015.1050394

Motivation-focused thinking: Buffering against stress-related physical symptoms and depressive symptomology

2015· article· en· W1844085676 on OpenAlexafffund
Jeremy M. Hamm, Raymond P. Perry, Judith G. Chipperfield, Tara L. Stewart, Jutta Heckhausen

Bibliographic record

VenuePsychology and Health · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsWinnipeg Regional Health AuthorityUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologyDepressive symptomsStress (linguistics)Clinical psychologyDepression (economics)PsychotherapistDevelopmental psychologyAnxietyPsychiatry

Abstract

fetched live from OpenAlex

Developmental transitions are experienced throughout the life course and necessitate adapting to consequential and unpredictable changes that can undermine health. Our six-month study (n = 239) explored whether selective secondary control striving (motivation-focused thinking) protects against the elevated levels of stress and depressive symptoms increasingly common to young adults navigating the challenging school-to-university transition. Path analyses supplemented with tests of moderated mediation revealed that, for young adults who face challenging obstacles to goal attainment, selective secondary control indirectly reduced long-term stress-related physical and depressive symptoms through selective primary control and previously unexamined measures of discrete emotions. Results advance the existing literature by demonstrating that (a) selective secondary control has health benefits for vulnerable young adults and (b) these benefits are largely a consequence of the process variables proposed in Heckhausen et al.'s (2010) theory.

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 score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0020.000

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 head0.061
GPT teacher head0.377
Teacher spread0.316 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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