Subjective Rationalities of Nonadherence to Treatment and Vaccination in Healthcare Decision-Making
Notice bibliographique
Résumé
Objective: In this short report contributing to the literature on treatment and vaccination adherence, nonadherence was examined from the perspective of decision-making (DM) practice in healthcare. The objective of this study was to survey the rationalities given for treatment nonadherence and their association with DM practice. Methods: The Ottawa decision Support Framework was used as a theoretical background for the study. Multiple choice and open-text responses indicating nonadherence were drawn from vignette survey data. The results have been analyzed and reported as descriptive statistics and findings of data-driven content analysis. The number of observatory units was 1032 in the within-subject study design. Results: DM practice was predominantly associated with nonadherence to vaccination, whereas nonadherence to treatment was consistently associated with attitudinal reasons independent of DM practice. Nonadherence to vaccination was most often rationalized by prior negative experiences in simple DM scenarios. After other DM practices, nonadherence was rationalized by uncertainty and criticism about the benefits of the recommended vaccine. Mistrust toward healthcare providers stood out, first in treatment nonadherence generally and, second, in vaccination nonadherence after simple DM where the final decision was left to the patient. Conclusion: In medical DM, adherence to treatment and vaccination may be achieved through a recognition of patients’ previous healthcare encounters and potential trust-related concerns, which could pose a risk for nonadherence. To be able to observe these risks, patient engagement and mutual trust should be priorities in decision support in healthcare. Plain Language Summary: Research on treatment and vaccination adherence aim at increasing knowledge about improving adherence and treatment outcomes. This study examined explanations given for not adhering to treatment and an association between the explanations and medical decision-making practices. Decision-making practices are known to impact patient–physician interaction and the patients’ motivation to have an active role at the appointment. In a shared decision-making (SDM) practice, patients’ participation is encouraged. SDM is built on both medical expertise of the practitioner and individual views, values and preferences of the patient. As opposed to SDM, authoritarian decision-making refers to a practice in which decisions are made solely by the physician. In guided decision-making, the physician shares information with the patient but makes the final decision. In simple decision-making, the final decision is left to the patient after consultation. This empirical study used illustrated vignette survey data from Finland. Out of the 1935 respondents, 64% were female with an average age of 68. In the study design, nonadherence was presumed to depend on a decision-making practice presented. Primary findings showed that nonadherence to treatment is most correlated with attitudinal predetermination of the patient and mistrust toward healthcare providers. Nonadherence to vaccination had a stronger association with decision-making practices. After simple decision-making, declining vaccination was most often explained by prior negative experiences and mistrust toward healthcare providers. After other decision-making practices, explanations for declining included uncertainty and criticism about the benefits of the recommended vaccine. This study underscores the pivotal role of trust in the patient-physician interaction. Keywords: decision-making, interaction, treatment adherence, trust, vaccination
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».