Identifying factors influencing sustainability of innovations in cancer survivorship care: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: Moving innovations into healthcare organisations to increase positive health outcomes remains a significant challenge. Even when knowledge and tools are adopted, they often fail to become integrated into the long-term routines of organisations. The objective of this study was to identify factors and processes influencing the sustainability of innovations in cancer survivorship care. DESIGN: Qualitative study using semistructured, in-depth interviews, informed by grounded theory. Data were collected and analysed concurrently using constant comparative analysis. SETTING: 25 cancer survivorship innovations based in six Canadian provinces. PARTICIPANTS: Twenty-seven implementation leaders and relevant staff from across Canada involved in the implementation of innovations in cancer survivorship. RESULTS: The findings were categorised according to determinants, processes and implementation outcomes, and whether a factor was necessary to sustainability, or important but not necessary. Seven determinants, six processes and three implementation outcomes were perceived to influence sustainability. The necessary determinants were (1) management support; (2) organisational and system-level priorities; and (3) key people and expertise. Necessary processes were (4) innovation adaptation; (5) stakeholder engagement; and (6) ongoing education and training. The only necessary implementation outcome was (7) widespread staff and organisational buy-in for the innovation. CONCLUSIONS: Factors influencing the sustainability of cancer survivorship innovations exist across multiple levels of the health system and are often interdependent. Study findings may be used by implementation teams to plan for sustainability from the beginning of innovation adoption initiatives.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".