Mission Statement Day: the impact on medical students of an early exercise in professionalism
Bibliographic record
Abstract
BACKGROUND: The development of professional attitudes in medical students is an important aspect of medical education. AIMS: This qualitative study describes medical students' experience of Mission Statement Day. METHOD: The study was conducted using focus groups and key informant interviews. Thematic analysis identified key words, phrases, and concepts. The data was condensed into major themes and key quotes were identified to illustrate each theme. RESULTS: The process of creating a Mission Statement was more important than the Mission Statement. Three themes were identified; the central role of patients, bonding and group formation, and student ownership and valuing of the Mission Statement. Patient involvement was critical to exploring the disease and illness experience, and to stimulating discussion about compassion and professional relationships. Role modelling by faculty highlighted the value placed on this experience by the medical school. The experience was memorable, prompting the medical students to reflect on their personal values and their decision to enter medical school. CONCLUSIONS: Creation of a Mission Statement is a powerful way to introduce students to their future professional role, identify their values, and begin to develop a sense of professional identity. This memorable experience could be expanded to help students continue their professional growth.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".