The effect of general practice team composition and climate on staff and patient experiences: a systematic review
Bibliographic record
Abstract
BACKGROUND: Recent policy initiatives seeking to address the workforce crisis in general practice have promoted greater multidisciplinarity. Evidence is lacking on how changes in staffing and the relational climate in practice teams affect the experiences of staff and patients. AIM: To synthesise evidence on how the composition of the practice workforce and team climate affect staff job satisfaction and burnout, and the processes and quality of care for patients. DESIGN & SETTING: A systematic literature review of international evidence. METHOD: Four different searches were carried out using MEDLINE, Embase, Cochrane Library, CINAHL, PsycINFO, and Web of Science. Evidence from English language articles from 2012-2022 was identified, with no restriction on study design. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and data were synthesised thematically. RESULTS: In total, 11 studies in primary healthcare settings were included, 10 from US integrated healthcare systems, one from Canada. Findings indicated that when teams are understaffed and work environments are stressful, patient care and staff wellbeing suffer. However, a good relational climate can buffer against burnout and protect patient care quality in situations of high workload. Good team dynamics and stable team membership are important for patient care coordination and job satisfaction. Female physicians are at greater risk of burnout. CONCLUSION: Evidence regarding team composition and team climate in relation to staff and patient outcomes in general practice remains limited. Challenges exist when drawing conclusions across different team compositions and definitions of team climate. Further research is needed to explore the conditions that generate a 'good' climate.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".