Beyond summative decision making: Illuminating the broader roles of competence committees
Bibliographic record
Abstract
CONTEXT: Although competence committees (CCs) are most often conceptualised as group decision-making bodies, policy documents forward a more ambitious vision and outline several additional roles for CCs that support the continuous quality improvement of education programmes and promote formative feedback. However, whether these functions are encompassed in the everyday work of CCs is currently unknown. METHODS: This constructivist grounded theory study elaborates the range of roles taken up by CCs and illuminates the processes through which these roles are actualised. Two investigators observed 27 CC meetings (>80 hours) across seven diverse postgraduate programmes at a single Canadian institution. Following each observation, a semi-structured interview was conducted with one CC member. Data collection and analysis unfolded iteratively. RESULTS: In this study, CCs did much more than make summative decisions about progression and entrustment; they identified a variety of problems that arose at the levels of both the individual learner and the programme, and leveraged their knowledge of assessment data to develop solutions. The problem-solving work of CCs was enabled by the in-depth data review, synthesis and analysis work that occurred before scheduled meetings, outside protected academic or administrative time. Although this work often appeared invisible to those outside the committee, the insights gleaned from data review provided committee members with essential information about how their programme of assessment was unfolding in practice. CONCLUSIONS: Competence committees may be an untapped resource that can support assessment for learning, local evaluation of competency-based medical education (CBME) implementation and continuous quality improvement for programmes of assessment. However, the ability of CCs to engage in this work is fragile and is currently sustained by the willingness of faculty members to devote their time and energy to it. The resourcing of CCs may have profound implications for translation of the theory of programmatic assessment and CBME into practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".