Meaning-Making and Psychological Adjustment to Cancer: Development of an Intervention and Pilot Results
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To develop an intervention that uniquely addresses the existential impact of cancer through meaning-making coping strategies and to explore the intervention's impact on psychological adjustment. DESIGN: Descriptive, qualitative approach to develop the intervention; one-group pre- and post-test design to pilot test the intervention. SETTING: Patients' homes or ambulatory oncology clinics affiliated with a university health center in eastern Canada. SAMPLE: 18 participants who were newly diagnosed in the past three months (n = 14), had completed treatment (n = 1), or were facing recurrence (n = 3) of breast (n = 10) or colorectal (n = 8) cancer. METHODS: Data were collected during interviews using a prototype intervention for trauma patients, and content was analyzed on an ongoing basis to fit the needs of the cancer population. Pretest and post-test questionnaires were administered to determine the intervention's effect. MAIN RESEARCH VARIABLES: Meaning-making intervention (MMI), patients' background variables, disease- or treatment-related symptoms, and psychological adjustment. FINDINGS: The MMI for patients with cancer consisted of as many as four two-hour, individualized sessions and involved the acknowledgment of losses and life threat, the examination of critical past challenges, and plans to stay committed to life goals. At post-test, participants significantly improved in self-esteem and reported a greater sense of security in facing the uncertainty of cancer. CONCLUSIONS: Findings suggest that meaning-making coping can be facilitated and lead to positive psychological outcomes following a cancer diagnosis. IMPLICATIONS FOR NURSING: The MMI offers a potentially effective and structured approach to address and monitor cancer-related existential issues. Findings are useful for designing future randomized, controlled trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".