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Enregistrement W2181710882 · doi:10.47102/annals-acadmedsg.v36n9p725

Helping Learners in Difficulty – The Incidence and Effectiveness of Remedial Programmes of the Medical Radiation Sciences Programme at University of Toronto and the Michener Institute for Applied Sciences, Toronto, Ontario, Canada

2007· article· en· W2181710882 sur OpenAlexaffabout
Ewa Szumacher, Pamela Catton, Glen A. Jones, Renate Bradley, Jeremy Kwan, Fiona Cherryman, Cathryne Palmer, Joyce Nyhof‐Young

Notice bibliographique

RevueAnnals of the Academy of Medicine Singapore · 2007
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensPrincess Margaret Cancer CentreInstitute for Christian StudiesMichener InstituteUniversity of TorontoSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésRemedial educationMedicineMedical educationAcademic yearFamily medicineMathematics educationPsychology

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Academic difficulty can often be a significant problem for students in health professional programmes. Students in difficulty are often identified late in their training and run the risk of dismissal if remediation is not successful. Since the inception of the Medical Radiation Sciences Program (MRSP) at the University of Toronto, Faculty of Medicine, and the Michener Institute (MI) in 1999, a number of students have required remediation due to problems in the didactic or clinical component of their training. Not all remediation was successful, and a number of students have been dismissed. There is relatively sparse evidence in the educational literature regarding the nature of academic difficulties that health professional students encounter, and what constitutes appropriate remedial education. The purpose of this research was to evaluate the incidence and prevalence of remediation in the MRSP and the nature of the academic problems. In addition, this study looked at the type of remedial instruction that the Radiation Sciences Board of Examiners (BOE) recommended for these students as well as the effectiveness of these recommendations. MATERIALS AND METHODS: This study consisted of a review of the academic records of students who failed one or more courses and underwent pre-clinical or clinical remediation, and who were presented at the Medical Radiation Sciences Board of Examiners at the University of Toronto between September 1999 and December 2004. Data extraction forms were developed to obtain demographic information, the nature of the academic problems, the remedial recommendation, and their outcomes. RESULTS: This study identified 69 students who were presented to the BOE 95 times. Forty-four students (44/69, 64%) were from the Radiation Therapy stream, 16 students (16/69, 23%) were from the Nuclear Medicine stream and 9 students (9/69, 13%) were from the Radiographic Technology stream. Most of the remediation occurred due to pre-clinical 50 (50/69, 72%), clinical 15 (15/69, 22%) and both preclinical and clinical problems 4 students (4/69, 6%). Out of 54 students who required pre-clinical remediation, 40 (74%) were promoted. Out of 19 students who required clinical remediation, 10 (10/19, 53%) passed their remediation. Six students (6/69, 9%) were dismissed from the programme due to unsuccessful remediation; 2 due to pre-clinical and 4 due to clinical problems. Based on these results, the remediation process at the MRSP was successful; however, 6 students (6/69, 9%) were dismissed from the programme during the last 4 years despite lengthy unsuccessful remediation. CONCLUSION: Our study provided an important perspective about the remediation process at the MRSP at the Michener Institute for Applied Health Sciences. Despite its retrospective methodology, it attempted to identify the magnitude of learning problems that lead to remediation, and identified the efficacy of the remedial programmes.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,402
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,008
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,335
Écart entre enseignants0,298 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations24
Publié2007
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueAnnals of the Academy of Medicine SingaporeMême sujetInnovations in Medical EducationTravaux en français237 207