Identifying strategies to support implementation of interprofessional primary care teams in Nova Scotia: Results of a survey and knowledge sharing event
Bibliographic record
Abstract
Abstract Background Interprofessional primary care teams (IPCTs) work together to enhance care. Despite evidence on the benefits of IPCTs, implementation remains challenging. This research aims to 1) identify and prioritize barriers and enablers, and 2) co-develop team-level strategies to support IPCT implementation in Nova Scotia, Canada. Methods Healthcare providers and staff of IPCTs were invited to complete an online survey to identify barriers and enablers, and the degree to which each item impacted the functioning of their team. Top ranked items were identified using the sum of frequency x impact for each response. A virtual knowledge sharing event was held to identify strategies to address local barriers and enablers that impact team functioning. Results IPCT members (n = 117), with a mix of clinic roles and experience, completed the survey. The top three enablers identified were access to technological tools to support their role, standardized processes for using the technological tools, and having a team manager to coordinate collaboration. The top three barriers were limited opportunity for daily team communication, lack of conflict resolution strategies, and lack of capacity building opportunities. IPCT members, administrators, and patients attended the knowledge sharing event (n = 33). Five strategies were identified including: 1) balancing patient needs and provider scope of practice, 2) holding regular and accessible meetings, 3) supporting team development opportunities, 4) supporting professional development, and 5) supporting involvement in non-clinical activities. Interpretation This research contextualized evidence to further understand local perspectives and experiences of barriers and enablers to the implementation of IPCTs. The knowledge exchange event identified actionable strategies that IPCTs and healthcare administrators can tailor to support teams and care for patients.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".