Bibliographic record
Abstract
This case study describes Bluewater Health’s quest to weave the philosophy and practice of patient and family-centered care from the boardroom to the bedside by introducing Emily. Emily’s image is a composite of photographs of staff, physicians, volunteers, patients and families exemplifying that each has a role in Emily’s care. Emily represents every patient and family of the past, present, and future. Emily’s journey started with the launch of Bluewater Health‘s 2013-2015 strategic plan and moved throughout the organization as patient councils were established and the organization embedded three foundational patient and family-centered RNAO Best Practice Guidelines into daily practice with the support of over 100 best practice champions. The successful implementation of RNAO’s best practice guidelines earned Bluewater Health designation as a Best Practice Spotlight Organization. The organization took a risk in introducing the notion of Emily knowing that Emily could become a cliché. No one was prepared for what has come to be known as “the Emily effect.” Emily’s effect is now being realized in increased patient satisfaction and improved employee engagement scores helping to deliver on our Mission, We create exemplary healthcare experiences for patients and families every time. Bluewater Health is a fully accredited, 326-bed community hospital that cares for the residents of Sarnia-Lambton, Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".