Informatics Competencies for Service Innovation in Paramedicine
Bibliographic record
Abstract
Paramedicine in Canada and throughout the developed world is currently undergoing unprecedented transformation to its service delivery model, largely driven by the need to relieve healthcare systems from overcrowding, and ensure its availability for all citizens. These changes are facilitated by the ability of paramedic services to adopt a number of innovative technologies, and their ability to respond by adopting new service delivery models, which may entail the deployment of paramedics in various non-emergency roles or integration with other healthcare services. The purpose of this dissertation is to determine how paramedic services innovate, and how that innovation is influenced by technology in particular. To fulfill this purpose, a two-phase sequential explanatory mixed-methods study is conducted, with a quantitative phase followed by a qualitative phase. In the first phase a multilevel theoretical model consisting of constructs that measure Service Innovation Performance, Dynamic Capabilities, Information Technology (IT) Capabilities and Group-Level Healthcare Informatics Competencies was evaluated with WarpPLS 5.0. A dataset with participation from paramedic leaders of Canadian land-based paramedic services (n=43) and paramedics employed at these services (n=502) was used for this purpose. Findings from this phase indicate that the information technology related knowledge and skills possessed by paramedics have an impact on various organization level dynamic capabilities, as do various IT Capabilities that focus on the relationship between the paramedic service leadership and the IT service provider. In the second phase, a qualitative approach was taken to explore contextual and other factors that facilitate or inhibit the ability of a paramedic service to innovate. Results from this phase suggest that Canadian paramedic services primarily undertake innovative activities with a strong focus on assuring and improving patient care. The use of an electronic patient care record (ePCR) is an important resource, as it enables activities such as the improvement of the clinical skills of paramedics, as well as facilitates the generation of business cases for equipment investment. Further, the informatics competencies of paramedics greatly facilitate the adoption of technology and equipment by individual services, as paramedics with a high amount of these competencies assist other paramedics when adopting technology, communicate innovative ideas within a service, and identify areas in need of change. The results of this dissertation underline the value of technology-related knowledge and skills for paramedics, and the importance of technology in ensuring that paramedic services provide a high and continually improving standard of patient care.
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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 | 0.003 | 0.014 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".