Making It Happen: Middle Managers' Roles in Innovation Implementation in Health Care
Bibliographic record
Abstract
BACKGROUND: Middle managers are given scant attention in the implementation literature in health care, where the focus is on senior leaders and frontline clinicians. AIMS: To empirically examine the role of middle managers relevant to innovation implementation and how middle managers experience the implementation process. METHODS: A qualitative study was conducted using the methods of grounded theory. Data were collected through semistructured interviews with middle managers (N = 15) in Nova Scotia and New Brunswick, Canada. Participants were purposively sampled, based on their involvement in implementation initiatives and to obtain variation in manager characteristics. Data were collected and analyzed concurrently, using an inductive constant comparative approach. Data collection and analysis continued until theoretical saturation was reached. RESULTS: Middle managers see themselves as being responsible for making implementation happen in their programs and services. As a result, they carry out five roles related to implementation: planner, coordinator, facilitator, motivator, and evaluator. However, the data also revealed two determinants of middle managers' role in implementation, which they must negotiate to fulfill their specific implementation roles and activities: (1) They perform many other roles and responsibilities within their organizations, both clinical and managerial in nature, and (2) they have limited decision-making power with respect to implementation and must work within the parameters set by upper levels of the organization. LINKING EVIDENCE TO ACTION: Middle managers play an important role in translating adoption decisions into on-the-ground implementation. Optimizing their capacity to fulfill this role may be key to improving innovation implementation in healthcare organizations.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".