Lessons learned from the implementation of Canada's first alongside midwifery unit: A qualitative explanatory study
Bibliographic record
Abstract
BACKGROUND: In July 2018, Canada's first midwife-led alongside midwifery unit (AMU) opened at Markham Stouffville Hospital (MSH) in Markham, Ontario. Our objectives were to examine how the conditions at MSH made it possible for the hospital to create the first AMU in Canada and to identify lessons to inform spread by examining how characteristics of the intervention, the inner and outer settings, the individuals involved, and the processes used influenced the MSH-AMU implementation process. METHODS: We conducted key informant interviews and document analysis using Yin's research methods. We used the Consolidated Framework for Implementation Research to conceptualize the study and develop semi-structured interview guides. We recruited key informants, including midwives and other health professionals, hospital leaders, leaders of midwifery organizations, and consumers, by email using both purposive and respondent driven sampling. Interviews were digitally recorded and professionally transcribed. We identified documents through key informants and searches of Nexis Uni, Hansard, and Google databases. We analyzed the data using a coding framework based on Greenhalgh et al.'s evidence-informed theory of the diffusion of innovations. RESULTS: Between November 2018 and February 2019, we conducted fifteen key informant interviews. We identified thirteen relevant documentary sources of evidence, including news media coverage, website content, Ontario parliamentary records, and hospital documents. Conditions that influenced implementation of the AMU fell within the following domains from Greenhalgh's diffusion of innovations theory: the innovation, the outer context, the inner context - system antecedents for innovation and system readiness for innovation, communication and influence, linkage - design phase and implementation stage, and the implementation process. While several unique features of MSH supported innovation, factors that could be adopted elsewhere include organizational investment in the development of midwifery leadership skills, intentional use of change management theory, broad stakeholder involvement in the design and implementation processes, and frequent, open communication. CONCLUSIONS: The example of the MSH-AMU illustrates the value of utilizing best practices with respect to change management and system transformation and demonstrates the potential value of using implementation theory to drive the successful implementation of AMUs. Lessons learned from the MSH-AMU can inform successful spread of this innovative service model.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".