Designing a virtual course for essential care partners (ECPs) in long-term care (LTC): a pre-implementation study
Bibliographic record
Abstract
We describe our co-design process aimed at supporting the reintegration of essential care partners into long-term care homes during the COVID-19 pandemic. More specifically, using a co-design process, we describe the pre-design, generative, and evaluative phases of developing a virtual infection prevention and control course for essential care partners at our partnering long-term care home. For the evaluative phase, we also provide an overview of our findings from interviews conducted with essential care partners on the expected barriers and facilitators associated with this virtual course. : Results from these interviews indicated that the virtual course was viewed as comprehensive, detailed, engaging, refreshing, and reliable, and that its successful implementation would require appropriate resources and support to ensure its sustainability and sustainment. Findings from this study provide guidance for the post-design phase of our co-design process. Our careful documentation of our co-design process also facilitates its replication for other technological interventions and in different healthcare settings. Limitations of the present study and implications for co-designing in the context of emergent public health emergencies are explored in the discussion.
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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 | 0.042 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".