Factors influencing middle managers’ commitment to the implementation of innovations in cancer care
Bibliographic record
Abstract
OBJECTIVE: To identify and illuminate influences on middle managers' commitment to innovation implementation. METHODS: A qualitative study was conducted, employing the methods of grounded theory. Semi-structured interviews were used to collect data from middle managers (n = 15) in Nova Scotia and New Brunswick, Canada. Data were collected and analysed concurrently, using an inductive constant comparative approach. Data collection and analysis continued until theoretical saturation was reached. RESULTS: The data revealed middle managers contemplate two central issues in terms of their commitment to implementation, that is whether or not they fully engage in and support the implementation of a particular innovation. These issues are (1) ease of implementation and (2) potential benefit for patients. Middle managers' views and expectations related to ease of implementation are influenced by available resources, fit with setting, and stakeholder buy-in. Their views on patient benefit are influenced by external evidence of benefit and local gaps in care. CONCLUSIONS: These findings provide further insight into the factors that influence middle managers' commitment to innovation implementation, and how middle managers consider these factors in the context of their work settings.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".