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Record W2318037763 · doi:10.1097/ncc.0b013e318208f2b3

Life After Cancer

2011· article· en· W2318037763 on OpenAlexaff
Krista L. Wilkins, Roberta L. Woodgate

Bibliographic record

VenueCancer Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCancerMedicineSurvivorship curveCancer survivorPopulationCancer registryGerontologyFamily medicineEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Recent research shows that cancer survivors are at greater risk of developing cancer than the general population. Although recommended, many cancer survivors receive no regular cancer screening. Cancer survivors' perceptions of their second cancer risk are, in part, suspected to influence their participation in cancer screening. OBJECTIVE: This study was conducted to explore how cancer survivors define and interpret second cancer risk. METHODS: An interpretive descriptive approach was taken whereby semistructured interviews were conducted with 22 cancer survivors (16 women and 6 men) drawn from a provincial cancer registry. The sample ranged in age from 19 to 87 years. The cancer history of the participants varied. Data were analyzed using the constant comparative method of data analysis. RESULTS: The overall theme, "life after cancer-living with risk," described cancer survivors' sense that risk is now a part of their everyday lives. Two themes emerged from the data that speak to how cancer survivors lived with second cancer risk: (1) thinking about second risk and (2) living with risk: a family affair. CONCLUSIONS: Effective risk communication to support the decisions made by cancer survivors with respect to cancer screening is warranted. IMPLICATIONS FOR PRACTICE: Study results provide foundational knowledge about the nature of second cancer risk that may be used to develop and refine standards for survivorship care including how second cancer risk can be best managed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.053
GPT teacher head0.325
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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