Educators must consider patient outcomes when assessing the impact of clinical training
Bibliographic record
Abstract
CONTEXT: The concept of outcomes has been used in health care for over 140 years. The use of outcomes in assessing quality of care regained prominence in the 1960s based on Donabedian's framework of structures, processes and outcomes. In the 1990s, the use of outcomes in medical education gained great favour, although the outcomes used were not carefully defined. Recently, a debate has ensued about the costs and, thus, sustainability of current health care programmes, focusing on the (non-)necessity of services, missed prevention opportunities and the efficiency of treatment programmes. Measurements using education outcomes and health care outcomes must take these issues into account, preferably from a common framework. As health care becomes increasingly costly and even inefficient, issues of effectiveness are often neglected in policy making. METHODS: This paper uses peer-reviewed evidence and an outcomes framework to explore the implications of current realities for the makers of education policy in the health professions and for the staff who train health professionals. DISCUSSION: If the ultimate impacts of practices and policies in health professions education are not considered, how will we know if our education structures, processes and outcomes are optimal? This essay examines this question from the perspectives of three related issues. The first refers to the need for a common framework if the outcomes of patient and community care are to be evaluated properly. The second perspective refers to whether it is feasible to consider both patient-based outcomes and patient-reported outcomes in assessing the impact of education programmes, especially at more advanced levels of training. The third perspective concerns the challenges and limitations that may be encountered in focusing on patient outcomes as a measure of the impact of education. The concluding discussion suggests how the results of such longer-term impact studies should be interpreted as key validity checks on the quality and effectiveness of medical education and clinical education if we are to address the validity and efficiency of outcomes used in education and training.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".