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Record W2800237079 · doi:10.1371/journal.pone.0195992

Measuring a new facet of post traumatic growth: Development of a scale of physical post traumatic growth in men with prostate cancer

2018· article· en· W2800237079 on OpenAlexaff
Deirdre Walsh, AnnMarie Groarke, Todd G. Morrison, Garrett Durkan, Eamonn Rogers, Francis Sullivan

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of GalwayNational University of Ireland
KeywordsClinical psychologyAnxietyPsychometricsProstate cancerPsychologyHospital Anxiety and Depression ScalePosttraumatic growthScale (ratio)MedicineCancerPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: This study developed a measure of physical post traumatic growth (physical post traumatic growth inventory; P-PTGI) in men with prostate cancer. METHODS: A pool of items was created from themes identified in a qualitative study. A quantitative study was then conducted to assess the psychometric properties of the P-PTGI in a sample of 693 prostate cancer survivors. RESULTS: Tests of dimensionality revealed that the 20-item P-PTGI contained two factors: Health Autonomy and Health Awareness. Results demonstrated that scale score reliability for the P-PTGI and its subscales was excellent. In support of the scale's convergent validity, scores on the P-PTGI correlated positively with mindfulness and quality of life, and correlated negatively with depression and anxiety. A statistically significant correlation between the P-PTGI and another robust indicator of post traumatic growth attests to its concurrent validity. CONCLUSIONS: While further investigation of the P-PTGI's psychometric properties is required, preliminary findings are promising.

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)0.003
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.254
Teacher spread0.201 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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