Separate Estates: A Case Study Analysis of Competency Assessment Processes among Clinicians in a Canadian Academic Hospital
Bibliographic record
Abstract
This study examined competence and competency assessment of regulated health professionals (RHPs) through a case study approach that utilized the Boyatzis model of effective performance. In order to explore how competency assessment was enacted and understood, and how RHPs perceived and experienced competency assessment in a large Canadian academic hospital, the study used three sources of data: key informant interviews (n=19), focus group interviews (n=32), and a review of organizational documents. Participants included RHPs, managers, directors, executives, and other non-health participants, such as human resources professionals. Data were analyzed inductively using Braun and Clark’s (2006) theoretical thematic analysis, and deductively using Hsieh and Shannon’s (2005) directed content analysis, all in NVivo Version 11 for Windows. Data revealed the absence of common language for competence across the hospital’s RHP participants. Participants referred to regulatory concepts and competency frameworks when asked to define and describe competence or competency assessment in the hospital setting. At the same time, a strong organizational rhetoric around excellence was evident, as was frequent commentary on incompetence. There was little discussion on RHPs who fell between excellence and incompetence. When describing organizational competency assessment processes, participants were equivocal about what was assessed and whether this assessment focused on organizational values or practice competencies. Fundamental differences in competency assessment processes exist for unionized RHP employees, non-unionized RHP employees and appointed physicians who were not employees, creating a situation of “separate estates”. These estates made interprofessional collaboration difficult by reinforcing uniprofessional perspectives, preventing organizational level discussions of competence and limiting the organization’s ability to harmonize processes across RHPs. Competence did not feature as a prominent concern for most participants who expressed the view that as long as the individual remained in good standing with their regulatory body, competency was not a matter of organizational concern. Recommendations call for healthcare organizations to implement best practice approaches to competency assessment across all RHPs; to establish congruence between organizational and regulatory assessments of competence; and to align assessment language between healthcare workplaces and regulatory bodies in order to facilitate meaningful discussions concerning competence in the practice setting and address the issue of “separate estates”.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".