From “invincibility” to “normalcy”: Coping strategies of young adults during the cancer journey
Bibliographic record
Abstract
OBJECTIVE: Little research has been undertaken regarding the psychological impact of cancer on those stricken during the young adult years. Specifically, research on the coping strategies of young adults with cancer is limited. METHOD: In this qualitative, Grounded Theory study, we did not set out to examine coping; rather, it emerged as a major phenomenon in the analysis of interview data from 15 young adults with cancer. RESULTS: These young adults used various coping strategies to come to terms with the cancer diagnosis, management of the illness, its treatment, and treatment sequelae. The coping strategies varied considerably from person to person, depended on the stage(s) of the illness, and were rooted in their precancer lives. We were able to discern a pattern of coping strategies used by most participants. The prevailing goal for all participants was to achieve what they called "normalcy." For some, this meant major changes in their lives; for others it meant to "pick up" where they had left off before the cancer diagnosis. SIGNIFICANCE OF RESULTS: To aid the understanding of the issues that influence coping, we have developed a model to illustrate the bidirectional nature and the complexities of the coping strategies as they relate to the phases of the disease and the disease treatment. The model also affirms Folkman and Lazarus' coping theory.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".