Improving comprehensive primary care training and practice through an interprofessional collaborative table
Bibliographic record
Abstract
Context: In response to the primary care crisis in Canada, the Team Primary Care (TPC) initiative aimed to transform primary care training and education to equip Canada’s workforce for effective team-based care. As part of the strategy to reach this goal, the Interprofessional Collaborative Table (IPCT) was launched. Objective: This study9s objective was to explore the evolution of the Interprofessional Collaborative Table over the project life cycle. Study Design and Analysis: We undertook a retrospective case study using a developmental evaluation approach and design thinking for analysis. Dataset: We analyzed the following data: meetings attendance; characteristics of project representatives; and meeting transcripts. Population Studied: We evaluated seven IPCT meetings from December 2022 to January 2024 with 80 project representatives invited to attend across 29 project categories. The reach of organizations in which project representatives were based spanned across Canada. Intervention: The IPCT was established to sustain collaborative relationships and intentionally create space for projects across different disciplines within TPC to connect, share, and collaborate with one another. Outcome Measures: Our outcome measures were determined through empathy maps, task summaries, and a journey map. Empathy maps are design thinking tools which help to identify user experiences and perspectives. The task summaries depicted high-level developments. An empathy map and task summary were developed for each IPCT meeting and were used to create a collective journey map of the IPCT. Journey maps are a visual illustration of a user’s experience of sequential events overtime. Results: We depicted the evolution of high-level developments of the seven IPCT meetings over time and tasks outlined at the beginning of the IPCT journey were achieved towards the end. There is still space to work on opportunities including aligning accreditation and regulatory body requirements and addressing systemic barriers to Indigenous sovereignty. The IPCT began to better understand these needs, and they have made a commitment to continue these conversations. Conclusions: The IPCT was successful in creating a shared space for TPC project partners to learn from one another. The IPCT brought together disciplines across primary care and developed a community intending to continue collaborating beyond the TPC project.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".