247 Breaking barriers to speaking up for safety: a leadership toolkit to foster a culture of communication openness
Bibliographic record
Abstract
Introduction Appropriate and timely communication between healthcare professionals is required to effectively respond to patient care concerns. Ineffective communication of care concerns can result in delayed diagnosis and treatment, or the inability to rescue a patient from a deteriorating condition (Johnston et al., 2015). Despite the recognition of its importance, there remains multiple barriers preventing communication openness between healthcare professionals. An organization’s safety culture is one large factor that can hinder or support effective communication. Further, healthcare leaders have a crucial role in influencing safety culture (Pozzobon et al., 2024).Aim At our large multi-site academic health sciences centre in Canada, we identified an opportunity to improve communication openness following a review of the results from an organization-wide safety culture survey (using the Agency for Healthcare Research & Quality (AHRQ 2021) safety culture survey) conducted in Fall 2023. We aimed to improve the scores on the communication openness domain of the survey. Recognizing leaders are key influencers in the development of a culture supportive of communication openness, we co-designated a toolkit reflective of best leadership practices to improve communication openness. This intervention aligns with our organization’s strategy and executive goals to deliver high quality care improving patient outcomes and experiences.Methods To develop the toolkit, a multi-disciplinary team was struck and was composed of leaders spanning the enterprise, a patient partner and experts in patient safety. The team decided to meet with those clinical leaders who have oversight of clinical areas that scored well on the communication openness domain of the safety culture survey, and those who have the greatest opportunity for improvement to inform the toolkit contents. Further, a review of the literature identified key leadership practices to support communication openness.Results The interviews and literature informed the development of a toolkit with three parts. Part one is a leadership self-assessment where leaders assess themselves on six domains using pre-determined questions. Part two provides resources and tools aligned with each of the six domains. Leaders are encouraged to select resources and tools aligned with the domain(s) where they have the greatest opportunity for improvement identified in part one. In part three, leaders are asked to develop an action plan using a template to improve communication openness and are encouraged to incorporate the tools and resources from part two. The toolkit was launched in Fall 2024. To support implementation, the organizations’ quality governance structures was leveraged. To evaluate the effectiveness of the toolkit, optional pulse surveys using the communication openness questions are underway and the organization wide safety culture survey will be repeated in 2025.References Agency for Healthcare Research. (2021). Hospital survey 2.0: 2021 user database report https://www.ahrq.gov/sites/default/files/wysiwyg/sops/surveys/hospital/2021-HSOPS2-Database-Report-Part-I-508.pdf Johnston M, Arora S, Anderson O, King D, Behar N, Darzi A. Escalation of care in surgery: A systematic risk assessment to prevent avoidable harm in hospitalized patients. Ann Surg. 2015;261(5):831–8.Pozzobon LD, Sears K, Zuk A. Leaders’ role in fostering a just culture. Nursing Leadership (Toronto, Ont.) 2024;36(3):44–55. https://doi.org/10.12927/cjnl.2024.27289
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".