Development of a survey instrument to investigate the primary care factors related to differences in cancer diagnosis between international jurisdictions
Bibliographic record
Abstract
BACKGROUND: Survival rates following a diagnosis of cancer vary between countries. The International Cancer Benchmarking Partnership (ICBP), a collaboration between six countries with primary care led health services, was set up in 2009 to investigate the causes of these differences. Module 3 of this collaboration hypothesised that an association exists between the readiness of primary care physicians (PCP) to investigate for cancer - the 'threshold' risk level at which they investigate or refer to a specialist for consideration of possible cancer - and survival for that cancer (lung, colorectal and ovarian). We describe the development of an international survey instrument to test this hypothesis. METHODS: The work was led by an academic steering group in England. They agreed that an online survey was the most pragmatic way of identifying differences between the jurisdictions. Research questions were identified through clinical experience and expert knowledge of the relevant literature.A survey comprising a set of direct questions and five clinical scenarios was developed to investigate the hypothesis. The survey content was discussed and refined concurrently and repeatedly with international partners. The survey was validated using an iterative process in England. Following validation the survey was adapted to be relevant to the health systems operating in other jurisdictions and translated into Danish, Norwegian and Swedish, and into Canadian and Australian English. RESULTS: This work has produced a survey with face, content and cross cultural validity that will be circulated in all six countries. It could also form a benchmark for similar surveys in countries with similar health care systems. CONCLUSIONS: The vignettes could also be used as educational resources. This study is likely to impact on healthcare policy and practice in participating countries.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".