Team dynamics within quality improvement teams: a scoping review
Bibliographic record
Abstract
PURPOSE: This scoping review examines what is known about the processes of quality improvement (QI) teams, particularly related to how teams impact outcomes. The aim is to provide research-informed guidance for QI leaders and to inform future research questions. DATA SOURCES: Databases searched included: MedLINE, EMBASE, CINAHL, Web of Science and SCOPUS. STUDY SELECTION: Eligible publications were written in English, published between 1999 and 2016. Articles were included in the review if they examined processes of the QI team, were related to healthcare QI and were primary research studies. Studies were excluded if they had insufficient detail regarding QI team processes. DATA EXTRACTION: Descriptive detail extracted included: authors, geographical region and health sector. The Integrated (Health Care) Team Effectiveness Model was used to synthesize findings of studies along domains of team effectiveness: task design, team process, psychosocial traits and organizational context. RESULTS OF DATA SYNTHESIS: Over two stages of searching, 4813 citations were reviewed. Of those, 48 full-text articles are included in the synthesis. This review demonstrates that QI teams are not immune from dysfunction. Further, a dysfunctional QI team is not likely to influence practice. However, a functional QI team alone is unlikely to create change. A positive QI team dynamic may be a necessary but insufficient condition for implementing QI strategies. CONCLUSIONS: Areas for further research include: interactions between QI teams and clinical microsystems, understanding the role of interprofessional representation on QI teams and exploring interactions between QI team task, composition and process.
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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 | 0.046 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.029 | 0.035 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".