Is this really happening? Family-centered care during COVID-19: People before policy
Bibliographic record
Abstract
In the middle of a global pandemic, hospitals created policies for visitor restrictions to reduce the transmission of coronavirus to protect patients and staff and developed protocols allowing only one support person to call the critical care unit for patient updates. Late on a Tuesday afternoon, the Manager of Patient Experience received a phone call asking her to call Karri, the wife of one of our patients who was on a ventilator. Karri was struggling with updating her mother-in-law because she was very upset with the news she received, making it difficult to call her husband’s mom. Karri asked the nurse on the phone if her mother-in-law could call in to get updates and was bluntly advised, “No we only allow one family member to call in to get updates.” Although Karri understood the protocol, she wished it had been a different response. This narrative describes the feelings and emotions experienced by Karri, along with what the Middlesex Health did to put people before policy to reduce the suffering for Karri and provide family-centered care. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".