Financial toxicity among cancer patients, survivors and their families in the United Kingdom: a scoping review
Bibliographic record
Abstract
BACKGROUND: The aim of this scoping review was to identify key research gaps and priorities in order to advance policy and practice for people living with cancer in the UK. METHODS: The review adhered to PRISMA guidelines for scoping review. We searched MEDLINE, EMBASE, Scopus, Web of Science and Google Scholar on 16 July 2022. There were no restrictions in terms of study design and publication time; gray literature was included. The key words, 'financial' or 'economic', were combined with each of the following words 'hardship/stress/burden/distress/strain/toxicity/catastrophe/consequence/impact.' RESULTS: 29/629 studies/reports published during 1982-2022 were eligible to be included in the review. No study conducted a comprehensive inquiry and reported all aspects of financial toxicity (FT) or used a validated measure of FT. The most three commonly reported outcomes related to financial hardship were financial well-being (24/29), benefit/welfare (17/29) and mental health status (16/29). CONCLUSIONS: It is evident that FT is experienced by UK cancer patients/survivors and that the issue is under-researched. There is an urgent need for further research including rigorous studies which contribute to a comprehensive understanding about the nature and extent of FT, disparities in experience, the impacts of FT on outcomes and potential solutions to alleviate FT and related problems.
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 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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".