Improving evaluation of the CanMEDS collaborator role:reliability of the Interprofessional Collaborator Assessment Rubric (ICAR) andgender bias in multi-source feedback
Notice bibliographique
Résumé
Since the inception of the Royal College of Physicians and Surgeons of Canada (RCPSC) CanMEDS framework, there has been inequality between the assessment of the Medical Expert role and the six non-Medical Expert roles. The purpose of the study was to evaluate the reliability of the use of the Interprofessional Collaborator Assessment Rubric (ICAR) in a multi-source feedback (MSF) approach for assessing post-graduate medical residents’ CanMEDS Collaborator competencies. A secondary investigation attempted to determine whether characteristics of raters (i.e., experience, gender, or frequency of interaction with resident) had any influence on overall ICAR score. The ICAR is a 17- item (and global score) assessment tool utilizing a 9-point scale and two open-text responses. The study involved medical residents receiving ICAR assessments from three (3) rater groups (physicians, nurses, and allied health professionals) over a single fourweek rotation. Residents were recruited from four (4) unique medical disciplines. Of those participating residents, sixteen (16) residents were randomly chosen. Six (6) of those received at least two (2) assessments from each rater group and were included in the analysis. All nurses and allied health professionals in participating medical / surgical units were invited to participate and were excluded from analysis if they were absent for at least one week of normal shift work or explicitly stated they did not interact with resident. Physicians were self-appointed by the residents. Statistical analysis utilized Cronbach’s alpha, compared overall ICAR scores using one-way and two-way, repeated measures ANOVA, and logistic regression. Missing data using a single imputation stochastic regression method and was compared to the missing data from a pilot study using pair-sample t-test. Results revealed a high response rate (76.2%) with a statistically significant difference between the gender distributions in each rater group, male physicians (81.8%), female nurses (92.5%), and female allied health professionals (88.4%), p < .001. Missing data decreased from 13.1% using daily assessments to 8.8% utilizing an MSF process, p = .032. An overall Cronbach’s alpha coefficient of α = .981 revealed high internal consistency reliability. Each ICAR domain also demonstrated high internal consistency, ranging between .881 - .963. The profession of the rater yielded no significant effect with a very small effect size (F₂,₅ = 1.225, p = .297, η² = .016). The only significant, main-effect on overall ICAR score was found to the gender of the rater (F₁,₅ = 7.184, p = .008, η² = .045). Female raters scored residents significantly lower than male raters (6.12 v. 6.82). Logistic regression analysis revealed that male raters were 3.08 times more likely than female raters to provide an overall ICAR score of above 6.0 (p = .013) and 3.28 times more likely to score above 7.0 (p = .005). A significant interaction effect resulted from a two-way repeated measures ANOVA analysis involving the frequency of interaction between raters and residents across items (F = 2.103, p = .025, η² = .014). The study findings suggest that the use of the modified ICAR form in a MSF assessment process could be a feasible assessment approach to providing formative feedback to post-graduate medical residents on Collaborator competencies.
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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 | 0,273 | 0,417 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».