Canadian Medical Education Journal Major Contribution/Research Article Ego Identity Status of Medical Students in Clerkship
Bibliographic record
Abstract
Background: Medical students encounter a variety of experiences that have an impact on their emerging professional identity. Clerkship, in particular, presents opportunities for students to consider their career options and decide upon a career path. The process of developing their professional identity begins well before clerkship, however. Anecdotal evidence suggests that interests in medicine begin as early as childhood. This study retrospectively examines the decision-making process clerks make in choosing medicine as a career. Methods: A total of 76 clerks (36 male, 34 female, 6 not reported) responded to four open-ended and two follow-up questions that measure career interests and pursuits. Questions addressed when and how students developed interests in medicine and alternate careers before beginning medical school. An additional eight closed questions drawn from the Ego Status Extended Objective Measure of Ego Identity Status II (EOM-EIS-II) were administered. Content analyses and inter-rater reliability analyses were conducted to classify students according to Marcia’s1 four ego identity statuses. Results: Having obtained high inter-rater consistency (Cohen’s Kappa coefficient of 0.92), responses to the open-ended questions resulted in the classification of three identity statuses. In total, 49.3 % of students were in the ‘achieved ’ (high exploration and commitment to choices) status and 48.1 % were in the ‘foreclosed ’ (low
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.119 | 0.006 |
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".