Antecedents and Outcomes of Uncertainty in Older Adults With Cancer: A Scoping Review of the Literature
Bibliographic record
Abstract
PROBLEM IDENTIFICATION: Uncertainty is a major source of distress for cancer survivors. Because cancer is primarily a disease of older adults, a comprehensive understanding of the antecedents and outcomes of uncertainty in older adults with cancer is essential. . LITERATURE SEARCH: MEDLINE®, PsycINFO®, Scopus, and CINAHL® were searched from inception to December 2015. Medical Subject Headings (MeSH) terms and free text words were used for the search concepts, including neoplasms, uncertainty, and aging. . DATA EVALUATION: Extracted data included research aims; research design or analysis approach; sample size; mean age; type, stage, and duration of cancer; type and duration of treatment; uncertainty scale; and major results. . SYNTHESIS: Of 2,584 articles initially identified, 44 studies (30 qualitative, 12 quantitative, and 2 mixed-methods) were included. Evidence tables were developed to organize quantitative and qualitative data. Descriptive numeric and thematic analyses were used to analyze quantitative results and qualitative findings, respectively. Outcomes were reported under four main categories. CONCLUSIONS: Uncertainty is an enduring and common experience in cancer survivorship. Uncertainty is affected by a number of demographic and clinical factors and affects quality of life (QOL) and psychological well-being. . IMPLICATIONS FOR PRACTICE: Uncertainty should be considered a contributing factor to psychological well-being and QOL in older adults with cancer. Nurses are in a unique position to assess negative effects of uncertainty and manage these consequences by providing patients with information and emotional support.
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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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".