Factors affecting patient decisions to undergo testing for cancer symptoms: an exploratory qualitative study in Australian general practice
Bibliographic record
Abstract
BACKGROUND: Patients presenting to their GP are often concerned their symptoms may be due to cancer. However, there is a lack of evidence on the factors that influence patient decisions to undergo investigation for suspected cancer in the general practice setting. AIM: To identify the factors influencing patient decisions to undertake investigations for suspected cancer in general practice. DESIGN & SETTING: An exploratory qualitative, semi-structured interview study of patients attending rural and metropolitan general practices in Victoria, Australia. METHOD: A purposive sample of 15 general practice patients aged ≥40 years participated. Thematic analysis of transcripts drew on interpretative description methodology and shared decision-making (SDM) theory. RESULTS: Cancer-related concerns such as 'cancer worry' prompt patients to seek investigations from their GP. Participants prefer that their symptoms are investigated regardless of cancer risk. The perceived 'best test' provides the most reassurance. Trust and SDM enhance dialogue between patients and GPs about diagnostic testing strategies. Deterrents to testing included out-of-pocket costs, waiting time, travel time, and competing work and family demands. CONCLUSION: There may be a mismatch between efforts to rationalise investigation use and patient preferences for investigation. SDM that incorporates patient concerns, facilitators, and barriers to testing may ensure appropriate and timely investigation of cancer symptoms.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".