111 Why most Australians consider it valuable to identify harmless abnormalities with diagnostic tests: mixed-methods study
Bibliographic record
Abstract
Background and Aims Explaining that medical tests are unnecessary or harmful often fails to dissuade individuals from wanting to proceed with the testing. It seems that individuals still want these tests because they attach other beliefs and values to them, such as valuing the information they provide and believing the tests may be reassuring. This is a challenge for messaging about overdiagnosis, which tends to rely on risk/benefit framing, and seldom accounts for these broader beliefs and values. Unfortunately, not enough is presently known about such beliefs and values to account for them in messaging. We examined the attitudes of Australians towards finding harmless abnormalities on tests, and the broader beliefs linked to these attitudes. This examination enabled us to uncover many of the values and beliefs that motivate testing even where there is no obvious benefit for identifying disease. Methods We used a mixed-methods survey design. We examined attitudes to finding harmless abnormalities using a Likert question with free text follow-up to explain those attitudes. We also measured a range of health beliefs using other Likert questions. Associations between attitudes to identifying harmless abnormalities and other beliefs and demographics were analysed using regression. Free text was analysed using comparative content and interpretative analyses, to examine inter-group differences. Results Almost three-fifths of the N=655 participants considered it valuable to identify harmless abnormalities using tests. Under a quarter were ambivalent and almost a fifth believed identifying such abnormalities would be harmful. In regression, beliefs that it would be ‘valuable’ to find such abnormalities on tests were predicted by higher confidence in doctors, lesser concerns about overtreatment, and a stronger belief in the importance of gathering data about one’s own body. Age, healthcare training, education and income were also significant predictors. The comparative qualitative analyses suggested that individuals held these positive attitudes to finding harmless abnormalities on tests because they thought doing so would provide psychological reassurance, inform them about their own bodies, and allow them to monitor and manage the harmless abnormalities. We believe these beliefs were underpinned by difficulties believing that any abnormalities could be truly harmless, and several ideas and values related to the broader utility of medical testing. On the other hand, people who believed it harmful to find such abnormalities on tests thought it would make them anxious and predispose them to receiving unnecessary health care. Conclusions The findings have a range of implications for preventing overdiagnosis. Encouragingly, people who held negative attitudes to identifying harmless abnormalities were cognisant of overdiagnosis and psychological challenges that can arise from knowing about even benign ‘abnormalities’. However, the findings also show why overdiagnosis messages fail to resonate with many individuals. Many struggle with the notion that any abnormality could be ‘harmless’, believe they can minimise the risk of overtreatment, and expect to gain many additional benefits from finding such abnormalities. Messages about overdiagnosis should consider addressing the broader beliefs and values that promote unnecessary health care, alongside risks and benefit information.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".