Patients as Front-Line Owners and Partners in Improving Quality and Safety
Bibliographic record
Abstract
As patient partners, we are pleased by the success of the front-line ownership (FLO) approach in advancing safe care in a variety of initiatives and settings. The FLO underlying principles and approach deeply resonate with us as illustrated in the following quotes from the paper: "Nothing about me without me," "Most passionate change agents are not in roles that typically get invited to participate," "Inviting anybody who is interested in the problem at hand," "FLO creates a way to break down hierarchies, increase positive dialogue between diverse players in organizations, and encourage people who may not have felt empowered previously to come forward and problem-solve." It is not described in the article if and how patients and/or patient partners were involved; therefore, we call on the authors to follow up with that information because it can provide valuable lessons to others who will be looking at implementing FLO in their organizations. Based on our decade-long experience as patient partners at all system levels, on literature and leading practices (key references included) we argue that patients have an important role to play in improvement initiatives and recommend partnering with patients in all improvement efforts.
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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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 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".