111 Why most Australians consider it valuable to identify harmless abnormalities with diagnostic tests: mixed-methods study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».