Interactive digital tools to support empowerment of people with cancer: a systematic literature review
Bibliographic record
Abstract
PURPOSE: To identify and synthesise interactive digital tools used to support the empowerment of people with cancer and the outcomes of these tools. METHODS: A systematic literature review was conducted using PubMed, CINAHL, Web of Science, Cochrane, Eric, Scopus, and PsycINFO databases in May 2023. Inclusion criteria were patient empowerment as an outcome supported by interactive digital tools expressed in study goal, methods or results, peer-reviewed studies published since 2010 in cancer care. Narrative synthesis was applied, and the quality of the studies was assessed following Joanna Briggs Institute checklists. RESULTS: Out of 1571 records screened, 39 studies published in 2011-2022 with RCT (17), single-arm trial (15), quasi-experimental (1), and qualitative designs (6) were included. A total of 30 interactive digital tools were identified to support empowerment (4) and related aspects, such as self-management (2), coping (4), patient activation (9), and self-efficacy (19). Significant positive effects were found on empowerment (1), self-management (1), coping (1), patient activation (2), and self-efficacy (10). Patient experiences were positive. Interactivity occurred with the tool itself (22), peers (7), or nurses (7), physicians (2), psychologists, (2) or social workers (1). CONCLUSION: Interactive digital tools have been developed extensively in recent years, varying in terms of content and methodology, favouring feasibility and pilot designs. In all of the tools, people with cancer are either active or recipients of information. The research evidence indicates positive outcomes for patient empowerment through interactive digital tools. Thus, even though promising, there still is need for further testing of the tools.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".