Prioritizing Clinical Teaching Excellence: A Hidden Curriculum Problem
Bibliographic record
Abstract
Abstract: There have been many initiatives to improve the conditions of clinical teachers to enable them to achieve clinical teaching excellence in Academic Medical Centres (AMC). However, the success of such efforts has been limited due to unsupportive institutional cultures and the low value assigned to clinical teaching in comparison to clinical service and research. This forum article characterizes the low value and support for clinical teaching excellence as an expression of a hidden curriculum that is central to the cultural and structural etiology of the inequities clinical teachers experience in their pursuit of clinical teaching excellence. These elements include inequity in relation to time for participation in faculty development and recognition for clinical teaching excellence that exist within AMCs. To further compound these issues, AMCs often engage in the deployment of poor criteria and communication strategies concerning local standards of teaching excellence. Such inequities and poor governance can threaten the clinical teaching workforce's engagement, satisfaction and retention, and ultimately, can create negative downstream effects on the quality of patient care. While there are no clear normative solutions, we suggest that the examination of local policy documents, generation of stakeholder buy-in, and a culturally sensitive, localized needs assessment and integrated knowledge translation approach can develop a deeper understanding of the localized nature of this problem. The findings from local interrogations of structural, cultural and process problems can help to inform more tailored efforts to reform and improve the epistemic value of clinical teaching excellence. In conclusion, we outline a local needs assessment plan and research study that may serve as a conceptually generalizable foundation that could be applied to multiple institutional contexts.
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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 | 0.063 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".