Early exposure to community service learning in the medical curriculum: A model for orientation week introduction
Bibliographic record
Abstract
Community service learning programs in pre-clerkship medical education are increasingly recognized as important in creating physicians who recognize the effects of one’s environment on their health and further strive to advocate for these patients to receive access to social programs that can improve their outcomes. The University of Ottawa Aesculapian Society recognized that an excellent method for providing early exposure to service opportunities in one’s new community is through Orientation Weeks. Prior to this year, no Orientation Week across Ontario had a philanthropy focus. Philanthropy in most students’ eyes refers to monetary donation. Understandably, Orientation Week directors continuously make the decision that asking medical students to donate money during the first week of one of many financially demanding yeas is unrealistic. Ottawa decided to incorporate philanthropy into our Orientation Week in the more inclusive form of community service, allowing students to donate their time, rather than donating their money. In addition to ensuring that philanthropy still has the opportunity to be a fundamental component of bonding during Medical School Orientation Weeks, as it does at the Undergraduate degree level, our initiative also served to facilitate early exposure to the various organizations students could complete their community service learning placements with later in their first year. Here we present our model, uO-Serves (“uOttawa-Serves”) of an Orientation Week philanthropy initiative of time-based community service in hopes that other Medical Schools will consider implementing a similar initiative within their Orientation Weeks
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".