Bibliographic record
Abstract
Patient safety in Canada has improved. Yet, dramatic transformation in safety across the continuum of care remains elusive. Front-line ownership (FLO) as outlined by Zimmerman and colleagues represents a novel bottom-up, or "discovery," approach to surmounting the challenges of further improving patient safety. Zimmerman et al.'s rationale and pilot study results suggest, however, that answers to important questions are required prior to the general adoption of FLO. For instance, in FLO's front-line collaborations, what is senior leadership's role? Is it limited to support, or is there a critical role in setting priorities and networking outside organizational boundaries to avoid reinventing the wheel? Who is included in the FLO team? Are housekeepers, doctors and patients all key teammates and contributors to success? In the near term, health organizations' support for FLO should be balanced with more directive safety solutions, within a broad framework that values both evidence-based practice and the generation of practice-based evidence. In this context, the authors of this commentary probe particular dimensions of FLO's theory and practice to promote the best positioning of FLO to enhance its optimal application of knowledge to reduce harm and improve patient safety.
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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.055 | 0.056 |
| Insufficient payload (model declined to judge) | 0.010 | 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".