Microsystems culture change: a refined theory for developing person-centred, safe and effective workplaces based on strategies that embed a safety culture
Bibliographic record
Abstract
Background: Attending to culture is central to developing workplaces that are safe and effective – those that prioritise learning to support continuing quality, person-centred relationships and the wellbeing of providers and recipients of care. Culture at the microsystems level, where care is experienced and provided, directly impacts on staff and patients but is generally given much less attention than organisational cultures at the meso level. This paper presents a refinement of a previously published middle-range theory of culture change derived from a concept analysis of effective workplace culture. It draws on findings from a project that set out to embed a safety culture and grow quality improvement and leadership capability through a regional patient safety initiative in frontline teams across four acute NHS hospital trusts in south-east England. Aims and objectives: To refine theoretical understanding about how to recognise and develop effective workplace cultures at the microsystems level based on practical insights from the Safety Culture Quality Improvement Realist Evaluation (SCQIRE) project. Methods: The evaluation approach for the SCQIRE project combined realist evaluation and practice development methodology. Realist evaluation was selected to answer the question ‘what works for whom and why when embedding a safety culture, improvement capability and leadership in frontline teams?’ Key to this approach is the local development, testing and refinement of ‘CMO’ relationships between: contexts (C); mechanisms, for example triggers and explaining why components work (M); and outcomes (O). Drawing on project data, the enablers, attributes and consequences of an effective workplace culture have been used to critically examine the factors that contributed to frontline teams’ ability to create and sustain a safety culture. Findings: A total of 24 CMO relationships resulted in four emerging programme theories that described what worked, why and for whom in relation to: 1) frontline teams developing their safety culture; 2) facilitators working with frontline teams to embed safety culture, quality improvement and leadership; 3) organisations supporting frontline teams; and 4) the patient safety collaborative initiative. Conclusions: It is concluded that the close relationship between person-centred values, ways of working and continuing effectiveness mean it is not possible to develop a safety culture without also being person-centred in relationships. Other theoretical refinements proposed include greateremphasis on the role of appreciative active learning, person-centredness in everyday relationships and an integrated approach to learning, development and improvement embedded at both micro and meso levels. The theory strengthens individual enablers of safety culture, with particular attention given to quality clinical leadership based on an inclusive, participative, collaborative approach involving all stakeholders, and to facilitation that embraces all the skills required for learning, developing and improving with person-centred values. Organisational enablers emphasise the need for a corporate body of facilitators to support frontline teams, as well as the role of senior organisational leaders in enabling a bottom-up approach to supporting quality and innovation. Implications for practice: • Safety and person-centred values are interdependent with ways of working in relationships and ongoing team effectiveness. None of these can be considered without the others • Investment in quality clinical leadership is essential for the development of high-performing teams, safety culture, achievement of shared meanings and direction, and valuing of engagement of both staff and patients • Facilitators supporting frontline teams require corporate support and a wide range of skills including leadership, the ability to promote engagement in co-creating meaning, and appreciative learning that draws on the workplace as a powerful resource • Senior organisational leaders need to model organisational values in every situation but also be skilled at enabling frontline teams to become empowered through supporting a bottom-up approach to innovation and change
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".