The Posttraumatic Growth Inventory: an examination of the factor structure and invariance among breast cancer survivors
Bibliographic record
Abstract
OBJECTIVE: The present study tested the proposed five-factor structure and invariance of the Posttraumatic Growth Inventory (PTGI; Tedeschi & Calhoun, 1996) in a sample of physically active breast cancer survivors. METHODS: A sample of breast cancer survivors (N=470, Mage=57.3, SD=7.8 years) completed the PTGI and a demographic questionnaire. The factor structure, factorial invariance, and latent mean invariance were tested using maximum likelihood structural equation modeling. RESULTS: Preliminary analyses showed acceptable reliability for the PTGI subscales (alpha<0.83). Confirmatory factor analysis (CFA) supported the five related factors corresponding to: relating to others, new possibilities, personal strength, spiritual change, and appreciation of life (chi(2) (179)=822.53, CFI=0.97, NNFI=0.96, SRMR=0.05, RMSEA=0.09). Multigroup CFA supported the invariance of the PTGI across age groups, treatment type, time since diagnosis, and time since last treatment. CONCLUSIONS: These findings provide support for (1) the multidimensional nature and factorial validity of the PTGI, and (2) the use of the PTGI in future research examining posttraumatic growth within samples of physically active breast cancer survivors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".