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Record W2743167897 · doi:10.1097/ncc.0000000000000537

Dimensions of Posttraumatic Growth in Patients With Cancer

2017· article· en· W2743167897 on OpenAlexaff
Mehdi Heidarzadeh, Maryam Rassouli, Jeannine M. Brant, Farahnaz Mohammadi Shahboulaghi, Hamid Alavi Majd

Bibliographic record

VenueCancer Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsBrantford Energy (Canada)
Fundersnot available
KeywordsMedicinePosttraumatic growthOncologyClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Posttraumatic growth (PTG) refers to positive outcomes after exposure to stressful events. Previous studies suggest cross-cultural differences in the nature and amount of PTG. OBJECTIVE: The aim of this study was to explore different dimensions of PTG in Iranian patients with cancer. METHODS: A mixed method study with convergent parallel design was applied to clarify and determine dimensions of PTG. Using the Posttraumatic Growth Inventory (PTGI), confirmatory factor analysis was used to quantitatively identify dimensions of PTG in 402 patients with cancer. Simultaneously, phenomenological methodology (in-depth interview with 12 patients) was used to describe and interpret the lived experiences of cancer patients in the qualitative part of the study. RESULTS: Five dimensions of PTGI were confirmed from the original PTGI. Qualitatively, new dimensions of PTG emerged including "inner peace and other positive personal attributes," "finding meaning of life," "being a role model," and "performing health promoting behaviors." CONCLUSION: Results of the study indicated that PTG is a 5-dimensional concept with a broad range of subthemes for Iranian cancer patients and that the PTGI did not reflect all growth dimensions in Iranian cancer patients. IMPLICATIONS FOR PRACTICE: Awareness of PTG dimensions can enable nurses to guide their use as coping strategies and provide context for positive changes in patients to promote quality 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 score0.031
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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