Emergency medicine residents' beliefs about contributing to aGoogle DocsTM presentation: a survey protocol
Bibliographic record
Abstract
BACKGROUND: Web 2.0 collaborative writing technologies have shown positive effects on medical education. One such technology, Google Docs(™), offers collaborative writing applications that improve healthcare students' sharing of information. Since 2008, all graduating residents in emergency medicine in Canada have had access to an online Google Docs(™) slideshow designed to help them share summaries of landmark articles in preparation for their Royal College of Physicians and Surgeons of Canada certification exam. A recent evaluation showed that contributions to the presentation were low. OBJECTIVE: This study will identify the factors that influence residents' decision to contribute or not to contribute to this online collaborative project. METHODS: Using the Theory of Planned Behaviour, semistructured interviews will be conducted with 25 graduating emergency medicine residents in Canada. Content from the interviews will be analysed to determine the most important beliefs in relation to the defined behaviour. CONCLUSION: To our knowledge, this study will be the first to use a theory based framework to identify healthcare trainees' salient beliefs concerning their decision whether to contribute to an online collaborative writing project using Google Docs(™).
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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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".