Assessment of a peer review process among interns at an Australian hospital
Notice bibliographique
Résumé
Purpose. This study considered how a peer review process could work in an Australian public hospital setting. Method. Up to 229 medical personnel completed an online performance assessment of 52 Junior Medical Officers (JMOs) during the last quarter of 2008. Results. Results indicated that the registrar was the most suitable person to assess interns, although other professionals, including interns themselves, were identified as capable of playing a role in a more holistic appraisal system. Significant sex differences were also found, which may be worthy of further study. Also, the affirmative rather than the formative aspect of the assessment results suggested that the criteria and questions posed in peer review be re-examined. Conclusion. A peer review process was able to be readily implemented in a large institution, and respondents were positive towards peer review generally as a valuable tool in the development of junior medical staff. What is known about the topic? The literature generally concurs that peer review is a useful tool in professional development and can provide a rounded view from diverse sources about a peer’s professional performance. It has been implemented in at least one Canadian medical facility as a mandatory process. What does this paper add? Our study identifies who is considered the most suitable peer(s) to assess interns, various substantive issues about peer review and about the process itself, and raises questions about the voluntary v. mandatory nature of peer review. It is the first study to trial peer review amongst interns in an Australian hospital. What are the implications for practitioners? That peer review is a suitable tool in professional development and generally supported in our study, suggesting that it could be implemented into Australian healthcare practice. However, education about the nature and value of peer review would be required amongst healthcare professionals, and the use of peer review could imply greater managerial engagement in medical practice. Peer review is a more effective assessment tool than that currently employed in many Australian hospitals.
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 | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,001 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».