Information needs across the colorectal cancer care continuum: scoping the literature
Bibliographic record
Abstract
VAN MOSSEL C., LEITZ L., SCOTT S., DAUDT H., DENNIS D., WATSON H., ALFORD M., MITCHELL A., PAYEUR N., COSBY C., LEVI-MILNE R. & PURKIS M.E. (2012) European Journal of Cancer Care21, 296–320 Information needs across the colorectal cancer care continuum: scoping the literature Because cancer care requires a multifaceted approach, providing useful and timely information to people with colorectal cancer may be fragmented and inconsistent. Our interest was in examining what has and has not captured the attention of researchers speaking to the information needs of people with colorectal cancer. We followed Arksey and O'Malley's framework for the methodology of scoping review. Focusing solely on colorectal cancer, we analysed 239 articles to get a picture of which information needs and sources of information, as well as the timing of providing information, were attended to. Treatment-related information received the most mentions (26%). Healthcare professionals (49%) were mentioned as the most likely source of information. Among articles focused on one stage of the care continuum, post-treatment (survivorship) received the most attention (16%). Only 27% of the articles consulted people with colorectal cancer and few attended to diet/nutrition and bowel management. This study examined the numerical representation of issues to which researchers attend, not the quality of the mentions. We ponder, however, on the relationship between the in/frequency of mentions and the actual information needs of people with colorectal cancer as well as the availability, sources and timing of information.
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 | 0.114 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.074 | 0.079 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".