The effect of general practice team composition and climate on staff and patient experiences: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it