Barriers and enablers to implementing Interprofessional Collaborative Teams in Primary Care: A narrative review of the literature using the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
Abstract Background Interprofessional collaborative teams (teams) have been introduced across Canada to improve access to and quality of primary care. However, the quality and speed of team implementation has been challenging and has not kept pace with increasing access issues. The aim of this research was to use an implementation framework to categorize and describe barriers and enablers to team implementation in primary care. Methods A narrative review that prioritized systematic reviews and evidence syntheseswas conducted. A search using pre-defined terms was conducted using Ovid MEDLINE, and potentially relevant grey literature was identified through ad hoc Google searches and hand searching of health organization websites. The Consolidated Framework for Implementation Research (CFIR) was used to categorize barriers and enablers into five domains: (1) Features of Team Implementation; (2) Government, Health Authorities and Health Organizations; (3) Characteristics of the Team; (4) Characteristics of Team Members; and (5) Process ofImplementation. Results Data were extracted from 19 of 435 articles that met inclusion/exclusion criteria. Most barriers and enablers were categorized into two domains of the CFIR: Characteristics of the Team and Government, Health Authorities, and Health Organizations. Key themes identified within the Characteristics of the Team domain were team-leadership, including designating a manager responsible for day-to-day activities and facilitating collaboration; clear governance structures, technology supports and tools that facilitate information sharing and communication; and a combination of formal and informal methods of communication. Key themes within the Government, Health Authorities, and Health Organizations domain were professional remuneration plans, regulatory policy, and interprofessional education that encourage and incorporate interprofessional competencies and values. Conclusions Barriers and enablers to implementing teams using the CFIR were identified, which enables stakeholders and teams to tailor implementation of teams at the local level to impact the accessibility and quality of primary care.
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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 | 0.072 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".