Collective leadership to improve professional practice, healthcare outcomes and staff well-being
Bibliographic record
Abstract
BACKGROUND: Collective leadership is strongly advocated by international stakeholders as a key approach for health service delivery, as a response to increasingly complex forms of organisation defined by rapid changes in health technology, professionalisation and growing specialisation. Inadequate leadership weakens health systems and can contribute to adverse events, including refusal to prioritise and implement safety recommendations consistently, and resistance to addressing staff burnout. Globally, increases in life expectancy and the number of people living with multiple long-term conditions contribute to greater complexity of healthcare systems. Such a complex environment requires the contribution and leadership of multiple professionals sharing viewpoints and knowledge. OBJECTIVES: To assess the effects of collective leadership for healthcare providers on professional practice, healthcare outcomes and staff well-being, when compared with usual centralised leadership approaches. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, five other databases and two trials registers on 5 January 2021. We also searched grey literature, checked references for additional citations and contacted study authors to identify additional studies. We did not apply any limits on language. SELECTION CRITERIA: Two groups of two authors independently reviewed, screened and selected studies for inclusion; the principal author was part of both groups to ensure consistency. We included randomised controlled trials (RCTs) that compared collective leadership interventions with usual centralised leadership or no intervention. DATA COLLECTION AND ANALYSIS: Three groups of two authors independently extracted data from the included studies and evaluated study quality; the principal author took part in all groups. We followed standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care (EPOC) Group. We used the GRADE approach to assess the certainty of the evidence. MAIN RESULTS: We identified three randomised trials for inclusion in our synthesis. All studies were conducted in acute care inpatient settings; the country settings were Canada, Iran and the USA. A total of 955 participants were included across all the studies. There was considerable variation in participants, interventions and measures for quantifying outcomes. We were only able to complete a meta-analysis for one outcome (leadership) and completed a narrative synthesis for other outcomes. We judged all studies as having an unclear risk of bias overall. Collective leadership interventions probably improve leadership (3 RCTs, 955 participants). Collective leadership may improve team performance (1 RCT, 164 participants). We are uncertain about the effect of collective leadership on clinical performance (1 RCT, 60 participants). We are uncertain about the intervention effect on healthcare outcomes, including health status (inpatient mortality) (1 RCT, 60 participants). Collective leadership may slightly improve staff well-being by reducing work-related stress (1 RCT, 164 participants). We identified no direct evidence concerning burnout and psychological symptoms. We are uncertain of the intervention effects on unintended consequences, specifically on staff absence (1 RCT, 60 participants). AUTHORS' CONCLUSIONS: Collective leadership involves multiple professionals sharing viewpoints and knowledge with the potential to influence positively the quality of care and staff well-being. Our confidence in the effects of collective leadership interventions on professional practice, healthcare outcomes and staff well-being is moderate in leadership outcomes, low in team performance and work-related stress, and very low for clinical performance, inpatient mortality and staff absence outcomes. The evidence was of moderate, low and very low certainty due to risk of bias and imprecision, meaning future evidence may change our interpretation of the results. There is a need for more high-quality studies in this area, with consistent reporting of leadership, team performance, clinical performance, health status and staff well-being outcomes.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".