Co-worker Assessment and Physician Multisource Feedback
Notice bibliographique
Résumé
Background: The College of Physicians and Surgeons of Alberta’s Physician Achievement Review program (PAR) uses questionnaire data from several sources, including co-workers, to provide formative feedback to physicians to promote quality improvement and continuing professional development within the profession. The PAR co-worker assessment questionnaires (CAQ) used in PAR were developed over more than a decade and not been psychometrically evaluated since then. The CAQs have not been reviewed either on their own or across the nine medical specialties which comprise PAR. Aim: The purpose of this study was to more fully understand the CAQs’ psychometric profile including an exploration of the interprofessional constructs being measured and any changes in performance scores over time. Method: A purposive sample of co-worker data from 1341 physicians across nine medical specialties in Alberta was evaluated. Secured PAR databases containing CAQ data (n = 9674) were accessed and analyzed using univariate and multivariate parametric techniques to: (a) evaluate the psychometric profile of the CAQ within and across specialty grouping; (b) to determine if physician characteristics and co-worker familiarity were associated with PAR performance scores; and (c) to evaluate if a difference existed between Time1 PAR feedback and Time2 PAR performance. Results: Internal consistency of all CAQs remains extremely high (i.e., > 0.90) suggesting a potential unidimensionality. Generalizability coefficients were not as robust as were originally reported. Variance components across the collection of CAQs indicated that opportunity for CAQ revision to improve its reliability is better directed at the processes associated with data collection (e.g., assessor selection practices) rather than revising questionnaire items. Principal components analyses were conducted as a variable reduction procedure. Each CAQ demonstrated a number of redundant questionnaire items (range: 5 to 11), and while the components structure remained similar to the factor structure published in the original research, component labeling was updated to reflect the CanMEDS competencies supporting interprofessional practice (i.e., communicator and collaborator). In the case of family physicians and pediatricians, a new component emerged reflecting the consolidation of the communicator and professional roles into one previously unidentified component labelled: good doctor. Several independent t-tests, ANOVA, and linear regression analyses were conducted providing evidence that certain physician characteristics together with co-worker familiarity were significant predictors of CAQ performance scores within speciality groupings. Different medical specialities were influenced differently by socio-demographic characteristics relative to CAQ scores; however, across all medical specialties co-worker familiarity demonstrated a significant positive linear relationship with CAQ scoring. Finally, significant increases and decreases in CAQ scores from Time1 to Time2 were found depending upon specialty grouping. Conclusion: The CAQ provides reliable data to physicians relative to interprofessional collaboration. Opportunity exists to improve the reliability of these tools by addressing unique variance components generated by the unbalanced nature of multisource feedback collection processes. Given the manner in which the tools were constructed, future revision efforts ought to include focus groups of specialty-specific co-workers seeking to illuminate how different clinical contexts influence what interprofessionality uniquely means within that specialty grouping.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».