Exploring the Usefulness of Two Conceptual Frameworks for Understanding How Organizational Factors Influence Innovation Implementation in Cancer Care
Bibliographic record
Abstract
Moving knowledge into practice and the implementation of innovations in health care remain significant challenges. Few researchers adequately address the influence of organizations on the implementation of innovations in health care. The aims of this article are to (1) present 2 conceptual frameworks for understanding the organizational factors important to the successful implementation of innovations in health care settings; (2) discuss each in relation to the literature; and (3) briefly demonstrate how each may be applied to 3 initiatives involving the implementation of a specific innovation-synoptic reporting tools-in cancer care. Synoptic reporting tools capture information from diagnostic tests, surgeries, and pathology examinations in a standardized, structured manner and contain only the information necessary for patient care. The frameworks selected were the Promoting Action on Research Implementation in Health Services framework and an organizational framework of innovation implementation; these frameworks arise from different disciplines (nursing and management, respectively). The constructs from each framework are examined in relation to the literature, with each construct applied to synoptic reporting tool implementation to demonstrate how each may be used to inform both practice and research in this area. By improving our understanding of existing frameworks, we enhance our ability to more effectively study and target implementation processes.
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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 | 0.068 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".