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Record W1988053921 · doi:10.1017/s1478951508000199

Psychometric evaluation of the German version of the Life Attitude Profile–Revised (LAP-R) in prostate cancer patients

2008· article· en· W1988053921 on OpenAlexfundno aff
Anja Mehnert, Uwe Koch

Bibliographic record

VenuePalliative & Supportive Care · 2008
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersTrent University
KeywordsCronbach's alphaCoping (psychology)Clinical psychologyPsychologyDistressExploratory factor analysisGermanConfirmatory factor analysisQuality of life (healthcare)PsychometricsMedicinePsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: There has been an increasing interest in the measurement of patients efforts to find meaning during the experience of a life-threatening illness. The aim of this study was to validate the German version of the Life Attitude Profile-Revised (LAP-R), a multidimensional measure of meaning and purpose. METHODS: A total of 511 prostate cancer patients with an average age of 64 years filled in the questionnaire during outpatient follow up care (response rate 70%). RESULTS: Five of the original six dimensions were replicated by exploratory and confirmatory factor analysis: Coherence, Existential Vacuum, Choice/Responsibleness, Death Acceptance, and Goal Seeking. The Purpose dimension was not replicated. Most LAP-R subscales showed good internal consistencies with Cronbach's alpha between .80 and .82, whereas the reliability for Existential Vacuum (alpha=.69) and Goal Seeking (alpha=.74) was less sufficient, but still acceptable. Results show significant concurrent associations between all LAP-R dimensions and measures of emotional distress, coping, and health-related quality of life; however, moderate correlations were found only for Existential Vacuum and depression, and inversely for depressive coping and the mental health subscale. SIGNIFICANCE OF RESEARCH: The German LAP-R is a reliable and valid instrument that can be recommended for further use in research and clinical cancer care.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.349
Teacher spread0.308 · 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 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

Citations29
Published2008
Admission routes1
Has abstractyes

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