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Record W2024228376 · doi:10.1159/000351269

A Lines-of-Defense Model for Managing Health Threats: A Review

2013· review· en· W2024228376 on OpenAlexafffund
Jutta Heckhausen, Carsten Wrosch, Richard Schulz

Bibliographic record

VenueGerontology · 2013
Typereview
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsConcordia University
FundersNational Institute of Nursing ResearchNational Institute of Mental HealthNational Institute on AgingCanadian Institutes of Health Research
KeywordsDisengagement theoryPsychologyControl (management)Process (computing)GerontologyMedicineComputer science

Abstract

fetched live from OpenAlex

As older individuals face challenges of progressive disease and increasing disability and approach the end of their lives, their capacity for controlling their environment and own health and functioning declines. The Lines-of-Defense Model is based on the Motivational Theory of Life-Span Development and proposes that individuals can adjust their control striving to the progressive physical decline in distinctly organized cycles of goal engagement and goal disengagement that reflect sequentially organized lines of defense. This organized process allows individuals to hold onto and defend still feasible levels of physical health and functioning in activities of daily living, while adjusting to increasing impairments. As physical constraints become more severe towards the end of life, avoiding psychological suffering becomes the focus of individuals' strivings for control. The Lines-of-Defense Model can also be applied to the inverse process of growth in functioning during recovery and rehabilitation.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.329
GPT teacher head0.502
Teacher spread0.172 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations77
Published2013
Admission routes2
Has abstractyes

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