Goal adjustment influence on psychological well‐being following advanced breast cancer diagnosis
Bibliographic record
Abstract
OBJECTIVE: A diagnosis of advanced breast cancer (ABC) challenges a woman's ambitions. This longitudinal study explored (1) if goal adjustment disposition influenced psychological adjustment patterns among women with ABC and (2) if dispositional hope and optimism moderate effects of goal adjustment on psychological adjustment. METHODS: One hundred ninety three out of 225 women with ABC were assessed while they were awaiting/receiving initial chemotherapy, then again at 6 weeks, 3 months, 6 months, and 12 months post-baseline. Goal disengagement, goal reengagement, optimism, hope, and psychological adjustment (anxiety, depression, and positive affect) were assessed at baseline; psychological adjustment was reassessed at each follow-up. Latent growth curve modeling was used to examine the change of psychological adjustment and test the study objectives. RESULTS: High goal disengagement, low reengagement, and high optimism were associated with lower initial anxiety, while high goal disengagement and optimism predicted a slower rate of change in anxiety. High goal disengagement, reengagement, and optimism were associated with lower initial depression. High goal reengagement, optimism, and hope were associated with initial positive affect scores, while optimism predicted its rate of change. Optimism moderated the effect of goal disengagement on anxiety and depression, whereas hope moderated the effect of goal reengagement on positive affect. CONCLUSION: Goal disengagement and reengagement are two relatively independent processes influencing psychological well-being. These findings will help clinicians to tailor specific interventions to help women coping with the diagnosis of ABC.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".