Are there opportunities to improve care as patients transition through the cancer care continuum? A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Transitions in Care (TiC) are vulnerable periods in care delivery associated with adverse events, increased cost and decreased patient satisfaction. Patients with cancer encounter many transitions during their care journey due to improved survival rates and the complexity of treatment. Collectively, improving TiC is particularly important among patients with cancer. The objective of this scoping review is to synthesise and map the existing literature regarding TiC among patients with cancer in order to explore opportunities to improve TiC among patients with cancer. METHODS AND ANALYSIS: This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Review Extension and the Joanna Briggs Institute methodology. The PubMed cancer filter and underlying search strategy will be tailored to each database (Embase, Cochrane, CINAHL and PsycINFO) and combined with search terms for TiC. Grey literature and references of included studies will be searched. The search will include studies published from database inception until 9 February 2020. Quantitative and qualitative studies will be included if they describe transitions between any type of healthcare provider or institution among patients with cancer. Descriptive statistics will summarise study characteristics and quantitative data of included studies. Qualitative data will be synthesised using thematic analysis. ETHICS AND DISSEMINATION: Our objective is to synthesise and map the existing evidence; therefore, ethical approval is not required. Evidence gaps around TiC will inform a programme of research aimed to improve high-risk transitions among patients with cancer. The findings of this scoping review will be published in a peer-reviewed journal and widely presented at academic conferences. More importantly, decision makers and patients will be provided a summary of the findings, along with data from a companion study, to prioritise TiC in need of interventions to improve continuity of care for patients with cancer.
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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 | 0.125 | 0.097 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.023 |
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".