Thinking of selection and widening access as complex and wicked problems
Bibliographic record
Abstract
OBJECTIVES: 'Wicked problems' are complex in nature, have innumerable causes associated with multiple social environments and actors with unpredictable behaviour and outcomes, and are difficult to define or even resolve. This paper considers why and how the frameworks of complexity theory and wicked problems can help medical educators consider selection and widening access (WA) to medicine through fresh eyes to guide future policy and practice. We illustrate how 'wickedity' can frame the key issues in this area, and then address steps that education stakeholders might take to respond to and act on these issues. METHODS: We used the 10 properties of a wicked problem to frame common issues in the broad field of selection and WA in medicine. We drew heavily on literature from different disciplines, particularly education, and, through debate and reflection, agreed on the applicability of the theory for illuminating and potentially addressing outstanding issues in selection and WA. RESULTS: Framing medical school selection using the 10 properties of wicked problems is a means of shifting thinking from erroneous 'simple' solutions to thinking more contextually and receptively. The wicked problem framework positions selection as a multi-causal, complex, dynamic, social problem and foregrounds stakeholders' views and context as being highly relevant in medical school selection. CONCLUSIONS: The wicked problem lens shifts thinking and action from seeking one elusive, objective truth to recognising the complexity of medical school selection, managing uncertainty, questioning and considering 'issues' associated with medical school selection more productively. Although there are criticisms of this framework, labelling medical selection as 'wicked' provides original insights and genuine reframing of the challenges of this important, and high profile, aspect of medical education. Doing so, in turn, opens the door to different responses than would be the case if selection and WA were simple and readily tamed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".