The impact 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. Method Four different searches were carried out between December 2021 and March 2022 using MEDLINE, Embase, Cochrane, CINAHL PsycINFO and Web of Science. PRISMA guidelines were followed and data were synthesised thematically. Results Eleven studies set in primary care were included, 10 from US integrated healthcare systems, one from Canada. Findings indicate 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. Good team dynamics and team cohesion have a greater impact on job satisfaction and patient care coordination than team composition; stable team membership is also important. Better patient experiences are associated with female physicians. However, these same physicians are at higher risk of burnout. Conclusion Evidence regarding team composition and team climate in relation to staff and patient outcomes remains limited. Challenges exist when drawing conclusions across different team compositions and differing definitions of team climate. Future research may benefit from exploring the conditions that generate a productive team climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".