Self and peer assessment of pediatricians, psychiatrists and medicine specialists: Implications for self-directed learning

Claudio Violato, Jocelyn Lockyer

Research output: Contribution to journalArticle

57 Scopus citations

Abstract

Self-regulation in medicine depends on accurate self-assessment. The purpose of the present study was to examine the discrepancy between self and peer assessments for a group of specialist physicians from internal medicine (IM), pediatrics, and psychiatry clinical domains (i.e., patient management, clinical assessment, professional development, and communication). Data from 304 psychiatrists, pediatricians and internal medicine specialists were used. Each physician had data from an identical self and 8 peer (38 item/4 clinical domains assessment). A total of 2306 peer assessments were available. Physicians were classified into quartiles based on mean assessment peer data and compared with self-assessment data. The analyses showed that self and peer assessment profiles were consistent across specialties and domains. Physicians assessed in the lowest and highest quartiles (i.e., <25th and >75th) by colleagues tended to rate themselves 30-40 percentile ranks higher and lower than peers, respectively. This study suggests that practicing physicians are inaccurate in assessing their own performance. These data suggest that systems to provide practicing physicians with regular and routine feedback may be appropriate if we are to ensure physicians are able to accurately assess themselves in a profession in which self-regulation is predicated upon the assumption that physicians know their capabilities and limitations.

Original languageEnglish (US)
Pages (from-to)235-244
Number of pages10
JournalAdvances in Health Sciences Education
Volume11
Issue number3
DOIs
StatePublished - Aug 1 2006

Keywords

  • Continuing medical education
  • Continuing professional development
  • Internal medicine specialists
  • Pediatricians
  • Peer assessment
  • Psychiatrists
  • Self assessment
  • Self directed learning

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