‘Gearing Up’ to improve interprofessional collaboration in primary care: a systematic review and conceptual framework
Bibliographic record
Abstract
BACKGROUND: Interprofessional Primary Care Teams (IPCTs) have been shown to benefit health systems and patients, particularly those patients with complex care needs. The literature suggests a wide range of factors that may influence collaboration in IPCTs, however the evidence base is unclear for many of these factors. To target improvement efforts, we identify studies that demonstrate an association between suggested factors and collaborative processes in IPCTs. METHODS: A systematic review of 25 years of peer-review literature was conducted to identify studies that test associations between policy, organizational, care team and individual factors, and collaboration in IPCTs. We searched Medline, ProQuest subject, ProQuest abstract, CINAHL, HealthSTAR, and Embase electronic databases between January 1990 to June 2015 and hand-searched reference lists of identified articles. RESULTS: The electronic searches identified 1421 articles, nine of which met inclusion criteria. Eighteen factors were significantly associated with collaboration in at least one article. We present the findings within a proposed conceptual model of interrelated 'gears'. The model offers a taxonomy of factors that policy makers (macro gear), organizational managers (meso gear), care teams (micro gear) and health professionals (individual gear) can adjust to improve interprofessional collaboration in IPC teams. Thirteen of the eighteen identified factors were within the micro gear, or team level of decision-making. These pertained to formal processes such as quality audits and group problem-solving; social processes such as open communication and supportive colleagues; team attitudes such as feeling part of the team; and team structure such as team size and having a collaboration champion or facilitator. Fewer policy (eg governance), organizational (eg information systems, organizational culture) or individual (eg belief in interprofessional collaboration care and personal flexibility) level factors were identified. CONCLUSIONS: The findings suggest that individual IPCTs have opportunities to improve collaboration regardless of the organizational or policy context within which they operate. Evidence supports the importance of having a team vision and shared goals, formal quality processes, information systems, and professionals feeling part of the team. Few studies assessed associations between collaboration and macro and meso factors, or between factors across levels, which are priorities for future research.
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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 | 0.081 | 0.154 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.042 | 0.038 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 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".