The Effect of Risk Attitude and Uncertainty Comfort on Primary Care Physicians' Use of Electronic Information Resources
Notice bibliographique
Résumé
Background: Clinicians use information regularly in clinical care. New electronic information resources provided in push, pull, and prompting formats have potential to improve information support but have not been designed for individualization. Physicians with differing risk status use healthcare resources differently often without an improvement in outcomes.Questions: Do physicians who are risk seeking or risk avoiding and comfortable or uncomfortable with uncertainty use or prefer electronic information resources differently when answering simulated clinical questions and can the processes be modeled with existing theoretical models?Design: Cohort study.Methods: Primary care physicians in Canada and the United States were screened for risk status. Those with high and low scores on 2 validated scales answered 23 multiple-choice questions and searched for information using their own electronic resources for 2 of these questions. They also answered 2 other questions using information from 2 electronic information sources: PIER© and Clinical Evidence© .Results: The physicians did not differ for number of correct answers according to risk status although the number of correct answers was low and not substantially higher than chance. Their searching process was consistent with 2 information-seeking models from information science (modified Wilson Problem Solving and Card/Pirolli Information Foraging/Information Scent models). Few differences were seen for any electronic searching or information use outcome based on risk status although those physicians who were comfortable with uncertainty used more searching heuristics and spent less effort on direct searching. More than 20% of answers were changed after searching—almost the same number going from incorrect to correct and from correct to incorrect. These changes from a correct to incorrect answer indicate that some electronic information resources may not be ideal for direct clinical care or integration into electronic medical record systems.Conclusions: Risk status may not be a major factor in the design of electronic information resources for primary care physicians. More research needs to be done to determine which computerized information resources and which features of these resources are associated with obtaining and maintaining correct answers to clinical questions.
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 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 | 0,002 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».