Identifying Research Impacts Within a Global Cancer Survivorship Grants Portfolio
Bibliographic record
Abstract
PURPOSE Research impact assessments (RIAs) are exercises conducted to measure research outputs, outcomes, and impacts of research projects and programs. While use among funders has been growing, RIA adoption has been fragmented, and a global, multi-funder RIA on cancer survivorship research has not been conducted. The field is an ideal case-study for RIA because the number of cancer survivors worldwide is currently over 30 million and projected to increase, and has comparatively less investment than research on other CSO categories. We aim to examine the global evidence base produced by cancer survivorship projects funded by International Cancer Research Partnership (ICRP) partners to determine their broader research, economic, and social impacts. METHODS Our evaluation will measure the impacts of projects from ICRP funders for 2006-2018 coded to CSO codes 6.1 Patient Care of Survivorship Issues or 6.6 End-of-life Care, using the Canadian Academy of Health Sciences Framework. To ensure high methodological quality, we have utilized seven key steps for RIA implementation developed from our previous work, including setting the stage for analysis (considering context, goals, and stakeholders); framework selection; metrics/indicator selection; primary data collection; data synthesis; communicating results; and reflecting on best practices. Impact data will be sourced from Dimensions. Ai and Overton; two leading databases capturing global research and policy impacts. RESULTS Among 146 cancer research funders participating in the ICRP during 2006-2018, 103 funded a total of 5,814 projects coded to CSO codes 6.1 and 6.6. Anticipated results include the numbers of matching projects identified within Dimensions. Ai with linked publications, altmetrics, clinical trials, and datasets, and linked policy citations identified through Overton as well as an overview of the types and ranges of impacts found. Preliminary results show high coverage of ICRP projects within the Dimensions. Ai database, and the ability to identify impacts increasingly likely among public funders. CONCLUSION Understanding downstream impacts of funded research is critical to ensuring that public and charitable funds are making a tangible impact towards improving survivorship and end-of-life care for persons living with and beyond cancer. We hope our analysis is the first of several that may begin to link research impacts with future opportunities to improve patient-oriented care and research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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; both teacher heads agree on what is shown here.
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".