Factors Related to the Implementation and Use of an Innovation in Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: Nationally, efforts to implement an innovation in cancer surgery-a Web-based synoptic reporting tool-are ongoing in five provinces. The objective of the present study was to identify the key multilevel factors influencing implementation and early use of this innovation for breast and colorectal cancer surgery at two academic hospitals in Halifax, Nova Scotia. METHODS: We used case-study methodology to examine the implementation of surgical synoptic reporting. Methods included semi-structured interviews with key informants (surgeons, implementation team members, and report end users; n = 9), nonparticipant observation, and document analysis. A thematic analysis was conducted separately for each method, followed by explanation-building to integrate the evidence and to identify the key multilevel factors influencing implementation. An audit was performed to determine use. RESULTS: Key factors influencing implementation were these: Innovation-values fitFlexibility with the innovation and implementationThe innovation is not flawlessStrengthening the climate for implementationResource needs and availabilityPartner engagementSurgeon champions and involvementIn a 6-month period after implementation, 91.2% and 58.0% respectively of eligible breast and colorectal cancer surgeries were reported using the new tool. CONCLUSIONS: An improved understanding of the multilevel factors influencing the implementation of innovations is critical to planning effective change interventions in health care. Further study is needed to explore differences in the use of the innovation between breast and colorectal cancer surgeons. Findings will inform the study of additional cases of synoptic reporting implementation, enabling cross-case analyses and identification of higher-level themes that may be applied in similar settings or contexts.
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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 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".