Measurement and Monitoring of Safety Framework: a qualitative study of implementation through a Canadian learning collaborative
Bibliographic record
Abstract
BACKGROUND: The Measurement and Monitoring of Safety Framework (MMSF) aims to move beyond a narrow focus on measurement and past harmful events as the major focus for safety in healthcare organisations. There is limited evidence of MMSF implementation and impact. OBJECTIVE: We aimed to examine participants' perspectives and experiences to increase understanding of the adaptive work of implementing the MMSF through a learning collaborative programme in diverse healthcare contexts across Canada. METHODS: The Collaborative consisted of 11 teams from seven provinces. We conducted a qualitative study involving interviews with 36 participants, observations of 5 sites and learning sessions, and collection of documents. RESULTS: Collaborative sessions and coaching allowed participants to explore reliability, sensitivity to operations, anticipation and preparedness, and integration and learning, in addition to past harm, and move beyond a project and measurement oriented safety approach. Participants noted the importance of time dedicated to engaging stakeholders in talk about MMSF concepts and their significance to their settings, prior to moving to implementing the Framework into practice. While participants generally started with a small number of ways of integrating the MMSF into practice such as rounds or huddles, many teams continued to experiment with incorporating the MMSF into a range of practices. Participants reported changes in thinking about safety, discussions and behaviours, which were perceived to impact healthcare processes. However, participants also reported challenges to sharing the Framework broadly and moving beyond its surface implementation, and difficulties with its sustained and widespread use given misalignments with existing quality and safety processes. CONCLUSION: The MMSF requires a dramatic departure from traditional safety strategies that focus on discrete problems and emphasise measurement. MMSF implementation requires extensive discussion, coaching and experimentation. Future implementation should consider engaging local leaders and coaches and an organisation or system approach to enable broader reach and systemic change.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".