An Exploration of Interprofessional Education in Four Canadian Undergraduate Nursing Programs
Bibliographic record
Abstract
In Canada, formal inclusion of interprofessional education (IPE) curricula within undergraduate nursing programs has occurred since 2012. While there is evidence that Canadian university nursing programs are working to achieve the integration of IPE throughout undergraduate curricula, a gap exists in what is known about IPE integration within Northern Ontario nursing programs, particularly from the perspectives of faculty members and program administrators. This multiple case study explored how four undergraduate university nursing programs in Northern Ontario have integrated IPE into their curricula, including the opportunities and challenges of this work. Data collection occurred at each site between June 2016 and June 2017 and consisted of interviews with program directors (n = 3), focus groups (n = 10) and interviews (n = 3) with faculty members, review of available program documentation and websites, and on-site program observations. Thematic analysis was undertaken for each case and during the cross-case comparison stage. The cross-case synthesis resulted in the following themes: a) varied understandings of IPE, b) diverse IPE learning activities within curricula, c) the requirement for support and resources for IPE and research, d) student participation and leadership in IPE, and e) limited IPE evaluation. Faculty development, IPE research, student involvement, and administrative support are required to maintain and sustain IPE. Dissemination of results may encourage further research and dialogue on current IPE practices among nursing programs in Northern Ontario and beyond.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".