Assessing how health information needs of individuals with colorectal cancer are met across the care continuum: an international cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Studies evaluating health information needs in colorectal cancer (CRC) lack specificity in terms of study samples involving patients. We assessed how health information needs of individuals with CRC are met across the care continuum. METHODS: We administered an international, online based survey. Participants were eligible for the study if they: 1) were 18 years of age or older; 2) received a diagnosis of CRC; and 3) were able to complete the online health survey in English, French, Spanish, or Mandarin. We grouped participants according to treatment status. The survey comprised sections: 1) demographic and cancer characteristics; 2) health information needs; and 3) health status and quality of life. We used multivariable regression models to identify factors associated with having health information needs met and evaluated impacts on health-related outcomes. RESULTS: We analyzed survey responses from 1041 participants including 258 who were currently undergoing treatment and 783 who had completed treatment. Findings suggest that information needs regarding CRC treatments were largely met. However, we found unmet information needs regarding psychosocial impacts of CRC. This includes work/employment, mental health, sexual activity, and nutrition and diet. We did not identify significant predictors of having met health information needs, however, among participants undergoing treatment, those with colon cancer were more likely to have met health information needs regarding their treatments as compared to those with rectal cancer (0.125, 95% CI, 0.00 to 0.25, p-value = 0.051). CONCLUSIONS: Our study provides a comprehensive assessment of health information needs among individuals with CRC across the care continuum.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".