The role of motivation in the diffusion of innovations in Canada’s long-term care sector: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Long-term care facilities offer shelter and care for Canadian seniors; however, there are great variances in the quality of care that is provided to older adults across facilities. One factor that could contribute to this variation in quality is the diffusion and implementation of advice and innovations within this sector. This study sought to understand the motivations of identified opinion leaders within the Canadian long-term care sector to disseminate advice within their social networks. Research questions addressed specific drivers of motivation and the potential outcomes of having motivated opinion leaders present within interpersonal advice-seeking networks with respect to diffusion and implementation of innovations in the Canadian long-term care sector. METHODS: (Cranley et al. 2019; Dearing et al. 2017). Constant comparison analysis was used and supported by a theoretical framework developed from diffusion of innovation theory and the COM-B framework. RESULTS: The motivations of opinion leaders in the Canadian long-term care sector were represented across seven themes: obligations of the position, value of education, systemness, relationships, supportiveness, passion, and caring nature. CONCLUSIONS: This research provides further evidence that opinion leaders in the long-term care sector are motivated individuals and that they are using this motivation as a driver to create change and improve care practices. As residents of the long-term care sector continue to increase in number and complexity, the presence of motivated opinion leaders represents a promising outlook for the future through achieving specific outcomes such as the diffusion and implementation of innovations, an increased sense of community within the network, and increased readiness for the future.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".