Clarifying “meaning” in the context of cancer research: A systematic literature review
Bibliographic record
Abstract
OBJECTIVES: This article synthesizes the published literature related to the construct of meaning in the adult cancer population. METHODS: The databases CancerLit, CINAHL, Medline, PsychINFO, and the Journal of Psychosocial Oncology and PsychoOncology were searched to identify all studies related to meaning. The methodological aspects of all studies are described and the conceptual aspects are summarized only from those studies that met criteria for methodological rigor and validity of findings. The definitions for global meaning, appraised meaning, search for meaning, and meaning as outcome as proposed by Park and Folkman were used to interpret the findings. RESULTS: Of 44 studies identified, 26 met the criteria for methodological rigor. There is strong empirical and qualitative evidence of a relationship between meaning as an outcome of and psychosocial adjustment to cancer. SIGNIFICANCE OF RESULTS: The qualitative findings are considered useful for the development of psychosocial interventions aimed at helping cancer patients cope with and even derive positive benefit from their experience. However, variations in the conceptual and operational definitions, frequent reliance on homogeneous and convenience sampling, and the lack of experimental designs are considered to be methodological limitations that need to be addressed to advance the study of meaning in the context of cancer.
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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 | 0.058 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".