Bibliographic record
Abstract
Our College of Medicine has developed a set of goals and objectives for its undergraduate programme covering a number of roles. To help identify the strengths and weaknesses of the overall programme, a tool reflecting these objectives has been developed based on grouped student self-assessments. Grouped self-assessment data have been previously validated for programme evaluation purposes, with self-assessments corresponding to third-party evaluations. Evaluating the efficacy of an undergraduate programme is complex, especially when specific strengths and weaknesses are to be identified. Conventional sources of data, such as residency placements, failure rates, board and licensing examinations and course evaluations, are often non-specific, do not address non-Medical Expert roles, focus on process and are subjective. The current project specifically addresses the college’s goals and objectives, which include promoting roles other than that of Medical Expert. A total of 64 objectives worded in the form of questions were administered to students in the class of 2010 immediately prior to the start of their clerkship and to the class of 2009 upon completing clerkship. Evaluations were completed anonymously online. This self-assessment was completed by 49 pre-clerkship and 27 post-clerkship students, reflecting response rates of 82% and 47%, respectively. Prior to distribution, the items were reviewed for clarity and pilot-tested by clinical clerks. When completing the self-assessment, students were asked to rate the extent to which they were currently able to meet the requirements outlined by each item on a scale of 1 (Not at all) to 10 (Very much) and the extent to which they had achieved each objective on their first day of medical school. Prior analysis from the class of 2010 revealed that students perceived that their abilities increased significantly from their first day of medical school for nearly all items. Independent-samples t-tests were conducted to measure statistically significant changes. Results indicate that post-clerkship medical students rated their increase in abilities higher than pre-clerkship students on the general objective (t[47] = − 3.93, P = 0.000, d = 1.13) and the categories of doctor as Medical Expert (t[47] = − 2.64, P = 0.011, d = 0.77), Communicator (t[48] = − 3.35, P = 0.002, d = 0.96), Health Advocate (t[50] = − 4.02, P = 0.000, d = 1.11), Collaborator (t[50] = − 2.71, P = 0.009, d = 0.75) and Resource Manager (t[48] = − 3.40, P = 0.001, d = 0.96). Thus, perceived skill in these areas appears to increase after completing clerkship, which demonstrates known-groups validity for the instrument. Overall, both pre- and post-clerkship students reported large gains in their perceived ability for objectives reflecting medical expertise, but smaller gains were reported for items reflecting interpersonal skills. All categories were found to be internally consistent (Cronbach’s α > 0.70). In summary, results indicate that post-clerkship medical students rate their abilities more highly than pre-clerkship students. The class of 2010 will complete this self-assessment near graduation and their pre- and post-clerkship responses will be compared to measure changes in perceived ability over time.
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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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".