Emergency medicine residents’ beliefs about contributing to an online collaborative slideshow
Bibliographic record
Abstract
OBJECTIVE: Collaborative writing applications (CWAs), such as the Google DocsTM platform, can improve skill acquisition, knowledge retention, and collaboration in medical education. Using CWAs to support the training of residents offers many advantages, but stimulating them to contribute remains challenging. The purpose of this study was to identify emergency medicine (EM) residents' beliefs about their intention to contribute summaries of landmark articles to a Google DocsTM slideshow while studying for their Royal College of Physicians and Surgeons of Canada (RCPSC) certification exam. METHOD: Using the Theory of Planned Behavior, the authors interviewed graduating RCPSC EM residents about contributing to a slideshow. Residents were asked about behavioral beliefs (advantages/disadvantages), normative beliefs (positive/negative referents), and control beliefs (barriers/facilitators). Two reviewers independently performed qualitative content analysis of interview transcripts to identify salient beliefs in relation to the defined behaviors. RESULTS: Of 150 eligible EM residents, 25 participated. The main reported advantage of contributing to the online slideshow was learning consolidation (n=15); the main reported disadvantage was information overload (n=3). The most frequently reported favorable referents were graduating EM residents writing the certification exam (n=16). Few participants (n=3) perceived any negative referents. The most frequently reported facilitator was peer-reviewed high-quality scientific information (n=9); and the most frequently reported barrier was time constraints (n=22). CONCLUSION: Salient beliefs exist regarding EM residents' intention to contribute content to an online collaborative writing project using a Google DocsTM slideshow. Overall, participants perceived more advantages than disadvantages to contributing and believed that this initiative would receive wide support. However, participants reported several barriers that need to be addressed to increase contributions. Our intention is for the beliefs identified in this study to contribute to the design of a theory-based questionnaire to explore determinants of residents' intentions to contribute to an online collaborative writing project. This will help develop implementation strategies for increasing contributions to other CWAs in medical education.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".