Factors influencing cancer patients’ experiences of care in the USA, United Kingdom, and Canada: A systematic review
Bibliographic record
Abstract
The extent to which individual and structural factors influence cancer patients' reports of their experiences are not yet well understood. We sought to identify which groups of patients consistently report poorer experiences and whether structural care factors might also be associated with better or worse reports. We conducted a systematic review of literature in PubMed and Web of Science with the date of last search as 27th of February 2022 following PRISMA guidelines. We focused on studies from three established population-based surveys datasets and instruments. After screening 303 references, 54 studies met the inclusion criteria. Overall, being from an ethnic minority group, having a more deprived socioeconomic status, poorer general or mental health status, being diagnosed with poor prognosis cancers, presenting to care through an emergency route, and having delayed treatment were consistently associated with poorer cancer care experiences. Conversely being diagnosed with earlier stage disease, perceiving communication as effective, positive patient-provider relationships, and receiving treatment with respect were overall associated with better reports of cancer care experiences. Improvement efforts aimed at delivering better experiences of patient-centred care need to take account much more explicitly patients' differing characteristics, prognoses, and trajectories they take through their care journeys.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".