Awareness of the Signs, Symptoms, and Risk Factors of Cancer and the Barriers to Seeking Help in the UK: Comparison of Survey Data Collected Online and Face-to-Face
Bibliographic record
Abstract
BACKGROUND: Cancer is the second leading cause of death globally, causing an estimated 9.6 million deaths in 2018. Low cancer symptom awareness has been associated with poor cancer survival for all cancers combined. The Cancer Awareness Measure (CAM) is a validated, face-to-face survey used since 2008 to measure the UK public's awareness of the symptoms and risk factors of cancer as well as the barriers to seeking help. OBJECTIVE: The aim of this study is to explore whether online data collection can produce a representative sample of the UK population, compare awareness of cancer signs and risk factors and the barriers to seeking help between data collected online and face-to-face, and examine the relationships between awareness and demographic variables. METHODS: Differences in awareness of cancer signs, symptoms, and risk factors among samples were explored while adjusting for demographic differences (age, gender, ethnicity, educational level, marital status, and country of residence) to distinguish the effect of data collection method. Multivariate logistic regression models were used to calculate adjusted odds ratios for recall and recognition of signs and symptoms, risk factors, and barriers to seeking help. RESULTS: A total of 4075 participants completed the CAM, 20% (n=819) via face-to-face interviews and 80% online (n=3256; agency A: n=1190; agency B: n=2066). Comparisons of data collected using face-to-face interviews and online surveys revealed minor differences between samples. Both methods provided representative samples of the UK population with slight differences in awareness of signs, symptoms, and risk factors and frequency of help-seeking barriers reported. CONCLUSIONS: These findings support a move to online data collection for the CAM. The flexibility afforded will enable the CAM to explore a wider range of issues related to the prevention, early diagnosis, and treatment of 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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".