Programme evaluation using student self‐assessments
Notice bibliographique
Résumé
Our College of Medicine has developed a set of goals and objectives for its undergraduate programme covering a number of roles. To help identify the strengths and weaknesses of the overall programme, a tool reflecting these objectives has been developed based on grouped student self-assessments. Grouped self-assessment data have been previously validated for programme evaluation purposes, with self-assessments corresponding to third-party evaluations. Evaluating the efficacy of an undergraduate programme is complex, especially when specific strengths and weaknesses are to be identified. Conventional sources of data, such as residency placements, failure rates, board and licensing examinations and course evaluations, are often non-specific, do not address non-Medical Expert roles, focus on process and are subjective. The current project specifically addresses the college’s goals and objectives, which include promoting roles other than that of Medical Expert. A total of 64 objectives worded in the form of questions were administered to students in the class of 2010 immediately prior to the start of their clerkship and to the class of 2009 upon completing clerkship. Evaluations were completed anonymously online. This self-assessment was completed by 49 pre-clerkship and 27 post-clerkship students, reflecting response rates of 82% and 47%, respectively. Prior to distribution, the items were reviewed for clarity and pilot-tested by clinical clerks. When completing the self-assessment, students were asked to rate the extent to which they were currently able to meet the requirements outlined by each item on a scale of 1 (Not at all) to 10 (Very much) and the extent to which they had achieved each objective on their first day of medical school. Prior analysis from the class of 2010 revealed that students perceived that their abilities increased significantly from their first day of medical school for nearly all items. Independent-samples t-tests were conducted to measure statistically significant changes. Results indicate that post-clerkship medical students rated their increase in abilities higher than pre-clerkship students on the general objective (t[47] = − 3.93, P = 0.000, d = 1.13) and the categories of doctor as Medical Expert (t[47] = − 2.64, P = 0.011, d = 0.77), Communicator (t[48] = − 3.35, P = 0.002, d = 0.96), Health Advocate (t[50] = − 4.02, P = 0.000, d = 1.11), Collaborator (t[50] = − 2.71, P = 0.009, d = 0.75) and Resource Manager (t[48] = − 3.40, P = 0.001, d = 0.96). Thus, perceived skill in these areas appears to increase after completing clerkship, which demonstrates known-groups validity for the instrument. Overall, both pre- and post-clerkship students reported large gains in their perceived ability for objectives reflecting medical expertise, but smaller gains were reported for items reflecting interpersonal skills. All categories were found to be internally consistent (Cronbach’s α > 0.70). In summary, results indicate that post-clerkship medical students rate their abilities more highly than pre-clerkship students. The class of 2010 will complete this self-assessment near graduation and their pre- and post-clerkship responses will be compared to measure changes in perceived ability over time.
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 | 0,038 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».