Exploring the Value of Technology to Stimulate Interprofessional Discussion and Education: A Needs Assessment of Emergency Medicine Professionals
Bibliographic record
Abstract
BACKGROUND: The emergency department (ED) is an environment fraught with increasing patient volumes, competing priorities, fluctuating information, and ad hoc interprofessional clinical teams. Limited time is available to reflect on and discuss clinical experiences, policies, or research with others on the involved team. Online resources, such as webcasts and blogs, offer an accessible platform for emergency shift workers to engage in interprofessional discussion and education. OBJECTIVE: Our objective was to explore the current opportunities for shared learning and discussion and to discover the potential of online resources to foster and facilitate interprofessional education within an academic tertiary emergency department community. METHODS: A qualitative study using semistructured interviews was conducted to solicit participants' views of the current culture of IPE in the ED, the potential value of introducing new online resources and technology in support of IPE, and possible barriers to uptake. Participation was voluntary and participants provided verbal informed consent. RESULTS: Online resources discussed included webcasts, interactive discussion forums, websites, and dashboard with links to central repositories. Identified barriers to uptake of new online resources were an unwillingness to "work" off-shift, a dislike of static one-directional communication, concerns with confidentiality, and the suggestion that new resources would be used by only a select few. CONCLUSIONS: Owing to the sensitive dynamics of emergency medicine-and the preference among its professional staff to foster interprofessional discussion and education through personal engagement, in an unhurried, non-stressful environment-introducing and investing in online resources should be undertaken with caution.
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 | 0.037 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".