Bringing patient advisors to the bedside: a promising avenue for improving partnership between patients and their care team
Bibliographic record
Abstract
This paper presents an innovative model of care, which brings patients who have already been through a similar experience of illness (patient advisors) directly to the bedside of patients, where they are viewed as full-fledged members of the clinical team. As part of a pilot project, three patient advisors were recruited and met with patients who had sustained a traumatic amputation and were admitted to the only center of expertise in replantation of the upper limb in Canada. Several individual interviews and focus groups with patients and patient advisors have revealed very promising results. Indeed, patients have expressed tremendous appreciation for their meetings and interactions with patient advisors. They have stated feeling less isolated, having a better morale and increased hopefulness regarding the outcome of the care pathway. Patient advisors also felt a positive impact of their involvement. A larger study needs to be conducted to determine the impact of this model of care on patient adherence to treatment and on members of the health care team.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".