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Record W2293586427

Control strategies for managing physical health problems in old age: Evidence for the motivational theory of life-span development

2016· book-chapter· en· W2293586427 on OpenAlexfundno aff
Meaghan Barlow, Carsten Wrosch, Jutta Heckhausen, Richard Schulz

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsLife spanPsychologyPhysical healthControl (management)GerontologySuccessful agingDevelopmental psychologyMedicineMental healthComputer sciencePsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

This chapter addresses how older adults manage the occurrence of physical health threats. Based on the motivational theory of life-span development (Heckhausen, Wrosch, & Schulz, 2010), we show how an opportunity-adjusted use of control strategies prevents older adults from experiencing the adverse psychological and physical consequences of confronting age-related declines in their physical health. We begin this chapter by outlining some of the basic theoretical assumptions of the motivational theory of life-span development, proposing a model for managing physical health threats in older adulthood. Next, we review the empirical literature on the effects of using control strategies for addressing physical health declines in the elderly. We finally suggest promising avenues for future research.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.128
GPT teacher head0.364
Teacher spread0.235 · 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
Published2016
Admission routes1
Has abstractyes

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