Co-creating scholarship through collaborative writing in health professions education: AMEE Guide No. 143
Bibliographic record
Abstract
This AMEE guide provides a robust framework and practical strategies for health professions educators to enhance their writing skills and engage in successful scholarship within a collaborative writing team. Whether scholarly output involves peer-reviewed articles, book chapters, blogs and online posts, online educational resources, collaborative writing requires more than the usual core writing skills, it requires teamwork, leadership and followership, negotiation, and conflict resolution, mentoring and more. Whilst educators can attend workshops or courses to enhance their writing skills, there may be fewer opportunities to join a community of scholars and engage in successful collaborative writing. There is very little guidance on how to find, join, position oneself and contribute to a writing group. Once individuals join a group, further questions arise as to how to contribute, when and whom to ask for help, whether their contribution is significant, and how to move from the periphery to the centre of the group. The most important question of all is how to translate disparate ideas into a shared key message and articulate it clearly. In this guide, we describe the value of working within a collaborative writing group; reflect on principles that anchor the concept of writing as a team and guide team behaviours; suggest explicit strategies to overcome challenges and promote successful writing that contributes to and advances the field; and review challenges to starting, maintaining, and completing writing tasks. We approach writing through three lenses: that of the individual writer, the writing team, and the scholarly product, the ultimate goal being meaningful contributions to the field of Health Professions Education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".