Measuring competence in healthcare learners and healthcare professionals by comparing self-assessment with objective structured clinical examinations: a systematic review
Notice bibliographique
Résumé
Executive summary Background The measure of clinical competence is an important aspect in the education of healthcare professionals. Two methods of assessment are typically described; an objective structured clinical examination and self-assessment. Objectives To compare the accuracy of self-assessed competence of healthcare learners and healthcare professionals with the assessment of competence using an objective structured clinical examination. Inclusion criteria Types of participants All healthcare learners and healthcare professionals including physicians, nurses, dentists, occupational therapists, physiotherapists, social workers and respiratory therapists. Types of intervention Studies in which participants were first administered a self-assessment (related to competence), followed by an objective structured clinical examination; the results of which were then compared. Types of outcomes Competence, confidence, performance, self-efficacy, knowledge and empathy. Types of studies Randomized controlled trials, non-randomized controlled trials, controlled before and after studies, cohort, case control studies and descriptive studies. Search strategy A three-step search strategy was utilized to locate both published and unpublished studies. Databases searched were: Medline, CINAHL, Embase, ERIC, Education Research Complete, Education Full Text, CBCA Education, GlobalHealth, Sociological Abstracts, Cochrane, PsycInfo, Mosby's Nursing Consult and Google Scholar. No date limit was used. Methodological quality Full papers were assessed for methodological quality by two reviewers working independently using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI). Data collection Details of each study included in the review were extracted independently by two reviewers using an adaptation of the standardized data extraction tool from JBI-MAStARI. Data synthesis Meta-analysis was not possible due to methodological and statistical heterogeneity of the included studies. Hence study findings are presented in narrative form. The data was also analyzed using 'The Four Stages of Learning' model by Noel Burch. Results The search strategy located a total of 2831 citations and 18 studies were included in the final review. No articles were removed based on the critical appraisal process. For both competence and confidence, the majority of studies did not support a positive relationship between self-assessed performance and performance on an OSCE. Conclusions Study participants' self-assessed competence or confidence was not confirmed by performance on an objective structured clinical examination. An accurate self-assessment may be threatened by over confidence and high performers tend to underestimate their ability. It is theorized that this disparity may in part be due to the stage that the learner or professional is in, with regard to knowledge and skill acquisition. Educators need to examine their evaluation methods to ensure that they are offering a varied and valid approach to assessment and evaluation. Notably, if self-assessment is to be used within programs, then learners need to be taught how to perform consistent and accurate self-assessments. Implications for practice It is important that educators understand the limitations within the evaluation of competence. Key aspects are the recognition of the stage that the learner is in with regard to skill acquisition and equipping both learners and professionals with the ability to perform consistent and accurate self-assessments. Implications for research There is a need for standardization on how outcomes are identified and measured in the area of competence. Further, identifying the leveling of an OSCE and the appropriate number of stations is required.
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 | 0,021 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».