Considerations for psychological safety with system-focused debriefings
Bibliographic record
Abstract
Systems integration simulations (SIS) and system-focused debriefing (SFDs) are tools to improve the processes and systems of healthcare.1–4 The goal of SIS/SFD is to identify systems issues/gaps, including latent safety threats to reduce preventable harm.1 2 5 6 Kolbe et al underscore the importance of psychological safety for effective learner-focused debriefings (LFD) and how this is built from a strong organisational culture.7 This may be even more prescient during SFD where participants are being asked for feedback about processes/systems that by their nature may reflect poorly on their leaders or organisation. Threats to psychological safety during SFD can inhibit the desire to openly share issues and undermine future improvement efforts.8 Little is published on how to manage psychological safety relative to a SFD. Kolbe et al describe strategies contributing to psychological safety before, during and after a LFD.7 This editorial highlights key considerations for managing psychological safety at each stage of SFD. Our perspective is based on a combined 40 years of experience conducting SIS/SFD. ### The pre-work phase The SIS/SFD early planning and engagement work, also called the pre-work phase is the starting point for establishing psychological safety.8 Inclusion and engagement of all key stakeholders and a clear endorsement from senior leaders help create a foundation for psychological safety.9 10 Threats to individual psychological safety may come from feeling left out, or that their opinion/role/stakeholder group was not held in high regard.11 Table 1 highlights the potential threats and mitigation strategies during all phases of SFD. View this table: Table 1 Potential threats to psychological safety during an SIS/SFD with suggested mitigation strategies/sample statements Pre-work includes a needs assessment to identify and prioritise anticipated highest risk/highest impact changes, which informs scenario design. Ensuring clarity of mission (ie, what types of issues can be mitigated, how they will be resolved, timelines) …
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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 | 0.162 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".