The physician assistant hospitalist: a time-motion study
Notice bibliographique
Résumé
Objective: The role of hospital-based physician assistants (PAs) is in need of delineation. To learn more about their activities, an administrative research project compared the tasks of hospitalists. In this setting an MD-PA team managed the adult medicine ward each weekday while three MDs rotated shifts.Methods: A priori a survey of hospitalist activities was administered to four providers in a medium sized hospital (3 MDs and 1 PA). This was followed by time-motion documentation that involved shadowing each member of the MD-PA hospitalist teams over a three-month period. A univariate analysis of activity patterns (perceived and observed) assessed what was perceived and what actually occurred on the wards. The mean, standard deviation, and difference in means for each task were calculated.Results: In the survey the PA reported she spent one-half of her hospitalist workdays on direct patient care and the physicians spent less time on direct patient care. Physicians believed they spent half days on direct patient care and believed the PA spent less time on direct patient care than they did. In the time-motion study shadowing the four hospitalists separately what was observed was that the PA spent 18% of her workday on direct patient care and 54% on indirect patient care - primarily patient encounter documentation. The three physicians spent 15% of their workday on direct patient care and 54% on indirect patient care – primarily patient encounter documentation. In summary, the perception of what each provider thought they did and what they in fact did differed significantly when actually observed. All four hospitalists (regardless of team composition) spent less than 20% of their workday with patients, and the rest divided among documentation, examination and test results, hospital meetings, and breaks. Task activity was similar for all providers except MDs attended more administrative meetings than the PA.Conclusions: The perception that physicians have of PA roles and what a PA actually does has been a reoccurring observation. A lack of understanding of PA role delineation by physicians may contribute to employment reluctance.
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 | 0,000 |
| 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 ».