Integrating Scientific Evidence to Support Telehomecare Development in a Remote Region
Bibliographic record
Abstract
This study aimed to understand how different types of knowledge have influenced the decision making process regarding the implementation of telehomecare in the organization of regional healthcare services in the Province of Quebec (Canada). A case study was conducted in order to explore how scientific evidence was integrated in the decision-making processes regarding the implementation of a telehomecare system in the Gaspésie-Magdalene Islands Health Region. A total of 14 semistructured interviews were completed with key organizational decision makers (regional managers, organization managers, healthcare professionals, and technological managers). Two researchers independently carried out data analysis, encouraging iterations and validation with study participants. The Gaspésie-Magdalene Islands Telehomecare Project is based on a technological solution named Intelligent Distance Patient Monitoring and constitutes a relevant example of the evolution of an e-health solution. Indeed, the first reports of the experiment influenced decision makers to continue the deployment of the solution. Decision makers from all groups agreed on the importance of using past experience to avoid pitfalls and ensure an optimal decision-making process. They highlighted the importance of knowledge translation between sites as well as within sites. Knowledge translation played an important part in the success of the project. Efficient strategies to transfer evidence to organizational decision making have been identified such as an endusers forum, where researchers provide support by sharing evidence with end-users and actively participate in knowledge translation.
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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 | 0.113 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".