Negative cancer beliefs, recognition of cancer symptoms and anticipated time to help-seeking: an international cancer benchmarking partnership (ICBP) study
Bibliographic record
Abstract
BACKGROUND: Understanding what influences people to seek help can inform interventions to promote earlier diagnosis of cancer, and ultimately better cancer survival. We aimed to examine relationships between negative cancer beliefs, recognition of cancer symptoms and how long people think they would take to go to the doctor with possible cancer symptoms (anticipated patient intervals). METHODS: Telephone interviews of 20,814 individuals (50+) in the United Kingdom, Australia, Canada, Denmark, Norway and Sweden were carried out using the Awareness and Beliefs about Cancer Measure (ABC). ABC included items on cancer beliefs, recognition of cancer symptoms and anticipated time to help-seeking for cough and rectal bleeding. The anticipated time to help-seeking was dichotomised as over one month for persistent cough and over one week for rectal bleeding. RESULTS: Not recognising persistent cough/hoarseness and unexplained bleeding as cancer symptoms increased the likelihood of a longer anticipated patient interval for persistent cough (OR = 1.66; 95%CI = 1.47-1.87) and rectal bleeding (OR = 1.90; 95%CI = 1.58-2.30), respectively. Endorsing four or more out of six negative beliefs about cancer increased the likelihood of longer anticipated patient intervals for persistent cough and rectal bleeding (OR = 2.18; 95%CI = 1.71-2.78 and OR = 1.97; 95%CI = 1.51-2.57). Many negative beliefs about cancer moderated the relationship between not recognising unexplained bleeding as a cancer symptom and longer anticipated patient interval for rectal bleeding (p = 0.005). CONCLUSIONS: Intervention studies should address both negative beliefs about cancer and knowledge of symptoms to optimise the effect.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".