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Record W3036359514 · doi:10.1002/pon.4476

Cancer Survivorship and Work

2017· article· en· W3036359514 on OpenAlexaff

Bibliographic record

VenuePsycho-Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill University
FundersUniversitair Medisch Centrum GroningenNational Research CentreRigshospitaletRijksuniversiteit GroningenSyddansk UniversitetCancer Research UK
KeywordsSurvivorship curveCancer survivorshipWork (physics)CancerMedicineEngineering

Abstract

fetched live from OpenAlex

Returning to work is recognized as a key aspect of cancer survivorship and returning to normality posttreatment. However, returning to work can present a number of challenges. WorkPlan is a workbook based intervention to support cancer survivors in the return to work process. This study aimed to explore the engagement and experiences of cancer survivors participating in a workbook based intervention (WorkPlan) to support return to work. Sixty-seven cancer survivors were recruited and randomized into either the intervention or the usual care arm of a feasibility RCT trial. Qualitative interviews (n = 23) were conducted with participants in the intervention arm at one month post-intervention and again at 12 months. Interviews at both times explored how participants managed their return-to-work and how engagement with the intervention provided supported that process. Interviews were audio recorded, transcribed verbatim and analyzed using a Framework approach. A total of 23 participants were interviewed. Results indicate that the workbook supported participants to create a return to work plan and communicate with their employer; participants cited this as crucial to managing their return-to-work. In addition, the workbook format of the intervention was well received with participants and they suggested ways in which hard copy materials and the act of writing were preferable to online interventions. The present study demonstrates how a workbook based intervention can support cancer survivors to successfully cope with a return to work following cancer treatment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.980

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)0.0010.000

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 head0.056
GPT teacher head0.395
Teacher spread0.339 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2017
Admission routes1
Has abstractyes

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