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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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