The simulated newsroom: A novel educational innovation to teach advocacy skills to resident physicians
Bibliographic record
Abstract
Abstract Need for Innovation Advocacy is a key competency of Canadian residency education, yet physicians seldom engage with supraclinical advocacy efforts upon completion of training. Objective of innovation The objective was to equip participants with the knowledge and skills required to engage as physician‐advocates in their communities using opinion writing as a tool. Developmental process We used Kern's six‐step framework to leverage a common medical training method, simulation, to teach journalistic skills related to advocacy in our novel “simulated newsroom.” Two emergency physicians with journalism training and workplace experience developed simulated newsroom workshops. The simulated newsroom consisted of participants acting as journalists and the expert facilitator acting as a news editor over two workshops. The participants were encouraged to write and workshop an article with colleagues. Evaluation Participants were invited to participate in a semistructured focus group and to submit their article for qualitative analysis. Focus group transcripts and written work were qualitatively analyzed to understand acceptability and feasibility and how participants might engage as future health advocates. Outcomes Twelve participants registered for the workshops and six attended. All six participated in the focus group; four submitted written work. The innovation bolstered participants' confidence in advocacy through the popular press and provided demonstrable skills in opinion writing. Participants valued the workshop as a voluntary component of residency education led by physicians with journalism expertise. Discussion The simulated newsroom may be an effective mechanism for increasing confidence and competence in advocacy writing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".