Bibliographic record
Abstract
Background: Although competency in written communication is a core skill, written communication is seldom the focus of formal instruction in medical education. The objective of this intervention was to implement a self-assessment strategy to assist learners in improving their letter writing skills and then to evaluate its feasibility, reliability and potential educational value. Methods: Eight first-year family medicine residents from two teaching sites completing a six month family medicine rotation used a self-assessment process which included a self-study module and an assessment tool for letters. Each resident applied the self-assessment tool to eight to ten consecutive consult/referral request letters. Participants submitted initial and redrafted letters for independent rating. Results: Analysis of the content, style and global ratings of the initial 77 draft letters showed multiple deficiencies in the content of their letters. It was confirmed that by using the self-assessment tool, residents were able to reliably assess the quality of their letters. Residents’ assessments and those of the expert closely correlated (Pearson correlation 0.861, p < 0.0001). Over the course of the study the residents’ overall performance improved and the difference in total scores between the initial drafts and the rewritten letters narrowed. Conclusion: A self-assessment process of written communication significantly improves the quality and completeness of routine consult/referral request letters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".