Efficacy and cost-effectiveness of an online mindfulness program (MindOnLine) to reduce fear of recurrence among people with cancer: study protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Fear of cancer recurrence (FCR) is a common condition among cancer survivors that can lead to significant levels of distress, anxiety and depression. Online mindfulness programmes may provide the mechanism to support cancer survivors manage FCR and distress, and improve people's well-being over the short, medium and long term. The primary aim of this study is to determine the potential efficacy of MindOnLine, a 9 session mindfulness-based programme for survivors of breast, prostate and colorectal cancer. A formal economic programme will also be conducted. METHODS AND ANALYSIS: A single-blind randomised controlled trial to determine the efficacy and cost-efficacy of a MindOnLine programme for cancer survivors. A total of 400 people living with cancer will be recruited via online advertisements on social media platforms, peak consumer advocacy groups or through outpatient services at healthcare providers across Victoria, Australia. People will be randomly allocated to either the MindOnLine programme (n=200) or waitlist control (n=200). Participant assessments will occur at baseline, at 9 weeks and 9-month follow-up. The primary outcome is change in Fear of Recurrence Index Score total score between baseline and 9 weeks; secondary outcomes are changes in depression and anxiety, quality of life and mindfulness. The economic analysis comprises a cost-consequences analysis where all outcomes will be compared with costs. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Peter MacCallum Cancer Centre (20-53) and Deakin University (2020-284). All participants will be required to provide written informed consent. Findings will be disseminated in peer reviewed journals and among key stakeholder organisations including hospitals, cancer and community organisations and Government. If successful the project will be rolled out nationally with a formal implementation plan. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (12620000645954); Pre-results. Registered 6 June 2020, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379520&isReview=true.
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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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.080 | 0.012 |
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".