A Digital Program (Hope) for People Living With Cancer During the COVID-19 Pandemic: Protocol for a Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 lockdown period in the United Kingdom that began on March 23, 2020, more than a quarter of a million people with cancer reported worsening mental health. Help to Overcome Problems Effectively (Hope) is a self-management program for people with cancer, designed to provide support for distress, unmet needs, and poor psychological health. In light of social distancing during the COVID-19 pandemic, digital delivery of the Hope Programme has become ever more vital for people with cancer. Previous pre-post studies of the digital Hope Programme have found reduced anxiety and depression and improved well-being for people with cancer. However, evaluation of this evidence has been limited by the lack of a control group in these previous studies. OBJECTIVE: We now present a protocol for a feasibility randomized controlled trial of the digital Hope Programme for people with cancer during the COVID-19 pandemic. Primary outcomes will be recruitment, dropout, and adherence rates, and estimations of sample and effect size. To detect signals of efficacy, secondary outcomes will be participant mental health and well-being. METHODS: Participants will be recruited by Macmillan Cancer Support (MCS) through their social media networks. The study will employ a feasibility wait-list randomized controlled trial (RCT) design, with people with cancer being randomized to join the digital Hope Programme immediately (intervention group [IG]) or join a 6-week waiting list (wait-list control group [WLCG]) with a 1:1 allocation ratio. Participants will complete digital measures of depression, anxiety, mental well-being, and confidence in managing their own health. Online questionnaires will be administered preprogram and 6 weeks postprogram. RESULTS: All people who had requested access to the Hope Programme from MCS (N=61) will be invited to participate in the trial. Baseline data collection commenced in April 2020, and the Hope Programme began for the IG in May 2020 and for the WLCG in June 2020. Postprogram data collection was completed by the end of August 2020. CONCLUSIONS: This feasibility study will provide data to inform the design of a future definitive trial. Wider-scale provision of the digital Hope Programme has potential to improve the lives of thousands of people with cancer and reduce the burden on health care providers during these unprecedented times. TRIAL REGISTRATION: ISRCTN Registry ISRCTN79623250; http://www.isrctn.com/ISRCTN79623250. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24264.
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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 | 0.069 | 0.056 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.112 | 0.020 |
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".