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Record W2346150427 · doi:10.2196/resprot.5300

A Guided Workbook Intervention (WorkPlan) to Support Work-Related Goals Among Cancer Survivors: Protocol of a Feasibility Randomized Controlled Trial

2016· article· en· W2346150427 on OpenAlexvenueno aff
Pernille Woods, Lauren Schumacher, Steven Sadhra, Andrew Sutton, Anjali Zarkar, Pauline Rolf, Elizabeth A. Grunfeld

Bibliographic record

VenueJMIR Research Protocols · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersResearch for Patient Benefit ProgrammeCoventry UniversityNational Institute for Health and Care Research
KeywordsRandomized controlled trialPsychological interventionWorkbookProtocol (science)Intervention (counseling)PsychologySelf-efficacyMedicineClinical psychologyPhysical therapyAlternative medicinePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Returning to and staying at work following illness is associated with better physical and psychological functioning. Not working has been shown to be associated with reduced self-esteem, lowered self-efficacy, and decreased belief in one's ability to return to the workplace. Although there is a growing body of research looking at what predicts return to work following cancer treatment, there are fewer studies examining interventions targeting return to work. OBJECTIVE: The primary objective is to assess the feasibility and acceptability of a theoretically led workbook intervention designed to support cancer patients in returning to work to inform a fully powered randomized controlled trial (RCT). METHODS: This is a multicenter feasibility RCT where the main analysis uses a qualitative approach. Sixty participants (aged 18-65 years) who have received a diagnosis of cancer and who intend to return to work will be randomized to either the WorkPlan intervention group or a usual care group (ratio 1:1). Participants in the intervention group will receive a guided workbook intervention (which contains activities aimed at eliciting thoughts and beliefs, identifying targets and actions, and concrete steps to achieve goals) and will receive telephone support over a 4-week period. The primary outcome measure is time taken to return to work (in days), and secondary outcome measures include mood, quality of life, illness perceptions, and job satisfaction. Data will be collected through postal questionnaires administered immediately postintervention and at 6- and 12-month follow-ups. In addition, interviews will be undertaken immediately postintervention (to explore acceptability of the intervention and materials) and at 12-month follow-up (to explore perceptions of participation in the trial and experiences of returning to work). RESULTS: Enrollment for the study will be completed in May 2016. Data analysis will commence in April 2017, and the first results are expected to be submitted for publication in late 2017. CONCLUSIONS: Currently no standardized return-to-work intervention based on targeting cancer patient beliefs is in existence. If the intervention is shown to be feasible and acceptable, the results of this study will inform a future full RCT with the potential to provide a valuable and cost-efficient tool in supporting cancer survivors in the return-to-work process. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): ISRCTN56342476; http://www.isrctn.com/ISRCTN56342476 (Archived by WebCite at http://www.webcitation.org/6gblhEPXd).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.042
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.032
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0420.006

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 head0.169
GPT teacher head0.540
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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

Citations10
Published2016
Admission routes1
Has abstractyes

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