Reading between the lines: exploring the unwritten rules of letters of recommendation in the Canadian resident selection process
Bibliographic record
Abstract
Background: Efforts to better understand and improve letters of recommendation (LORs) in the resident selection process have identified unwritten rules and hidden practices that may limit their effectiveness. The objective of our study is to explore these unwritten rules and hidden practices more fully in one Canadian academic medical community. Methods: We conducted semi-structured, discourse-based interviews with 18 faculty members from the departments of Internal Medicine and Psychiatry at the University of Manitoba, Canada. Interviews were guided by sample LORs and were focused on experiences with either writing or reading LORs. We analyzed interviews using key concepts from genre theory and Aristotle's appeals to ethos, logos, and pathos. Results: Participants described how the practices surrounding LORs are guided by unwritten rules. These practices contributed to writers' use of visible strategies and textual silence to establish credibility, build a strong case, and appeal to readers. Readers rely on similar strategies, but not always as intended by the writers. Conclusions: The unwritten rules of one academic community can impede a nationally-facilitated resident selection process. Our findings highlight how critiques and potential improvements to LORs could benefit from considering the use of visible and invisible rhetorical strategies in specific contexts.
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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 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.034 | 0.030 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".