Notice bibliographique
Résumé
Objective: Self-assessment is a readily available evaluation method that is seldom used in postgraduate education because of its poor concurrent and predictive validity. Self-assessment tools often use global performance checklists rather than specific statements of expected competencies. Our goal was to develop and implement a self-assessment tool for residents based on rotation-specific learning objectives. Description: In 1998 at Queen's University, a set of self-assessment tools for core surgical residents was developed, each based on the learning objectives of one specific clinical rotation. Before and after each core surgical rotation, residents estimated their competencies on the objectives of that rotation using a scale of 1 (totally incompetent) to 7 (totally competent). Immediately after each rotation, trainees also scored their perceived competencies on a set of 15 generic (rotation-independent) core surgical objectives. The assessment sheets were collected immediately after the in-training evaluation but were not used in the formal evaluation process. Discussion: In the first year of implementation, nine core residents rotating through seven surgical services completed 43 self-assessments. As expected, the second-year residents' mean scores were higher than the first-year residents' scores, and the mean post-rotation scores were higher than the prerotation scores. There was, however, significant variability among services, and statistically significant improvements were found for only two of the seven services. Self-reported gains in competency can provide valuable information for specific rotation evaluation and feedback. One expects to see a gradual improvement in the generic objectives through the two core training years. Trainees whose self-assessment appears to lag or decline will prompt focused discussion among program coordinators or advisers to identify the underlying problem(s). Self-assessment of individual rotation objectives provides significant feedback both to trainees and to faculty. Trainees readily identify their areas of weakness, and when such assessments are used formatively mid-rotation, they can deliberately focus their efforts on the problem areas. Teaching faculty can easily identify objectives that are consistently covered inadequately on their services, a process that can lead to exploration of underlying issues and potential program enhancements. Self-assessment can form an excellent basis for constructive, bidirectional, feedback. It allows faculty and trainees to explore together both true competencies as well as individual—and possibly erroneous—perceptions of competency. On their part, trainees can use their assessments to identify service-related issues worth discussing. Evaluation: The residents found the forms easy to complete, and the initial response rate was satisfactory. Compliance was far from ideal, however, and methods to enhance it are being sought. The system was easy to implement and required only minimal resources. The key issue that needs to be addressed next is that of concurrent validity of the tool with in-training evaluations and objective scores; the exact role that this self-assessment tool can play within the post-graduate evaluation process will then be possible to define.
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 | 0,005 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».