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Record W2973563514 · doi:10.3138/jmvfh.2018-0053

Veteran resilience following combat-related amputation

2019· article· en· W2973563514 on OpenAlexvenueno aff
Juliann M.C. Jeppsen, David Wood, Kalin B. Holyoak

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2019
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsAmputationPsychologyPsychological resiliencePerspective (graphical)Resilience (materials science)MedicineClinical psychologyPsychiatryPsychotherapistComputer science

Abstract

fetched live from OpenAlex

Introduction: Amputation following combat-related injury places substantial stress on survivors and their spouses. The purpose of this study was to explore the experiences of combat-related amputation among military Veterans and explore pathways to resilient behaviours. Methods: This qualitative study used a purposeful sample of male US military Veterans and their partners. We used the Metatheory of Resilience and Resiliency (MRR) as a conceptual framework for understanding the drives that promote growth through adversity and disruptions. MRR was also used to characterize each Veterans’ state of resilience after the amputation. Results: The majority of Veterans returned to their baseline level of functioning (reintegration back to homeostasis) and that some Veterans are functioning better than before the amputation (resilient reintegration). Discussion: Veterans who appear to have built a life post-amputation exhibited the following resilience drives: finding perspective and purpose (universal resilience), living consistent with one’s values and character strengths (character resilience), and accessing positive social support (ecological resilience). Practitioners should be alert to these themes among Veterans with traumatic amputation.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

Explore more

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