Bibliographic record
Abstract
In their paper "Front-Line Ownership: Generating a Cure Mindset for Patient Safety," Zimmerman and her colleagues introduce us to the novel concept of FLO - front-line ownership - within the quality and safety arena. Based on their 18-month study of nosocomial infections within five Canadian hospitals, the authors highlight the benefits of allowing front-line staff to own and manage patient safety problems as opposed to imposing programs on them that were created by leaders who did not consult them in developing appropriate solutions.Their paper highlights many of the benefits of FLO, particularly around social networking, interdisciplinary team work and clinician engagement. But how does FLO measure up in the context of other more technical methods of managing adverse events within healthcare organizations? What are the benefits and weakness of FLO? Is FLO consistent with external accreditation requirements and the drive for greater standardization? Will its necessarily longer time frame consign it to a few small-scale research projects or is there real potential to use FLO techniques for other quality and safety problems beyond nosocomial infections?
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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.068 | 0.083 |
| Insufficient payload (model declined to judge) | 0.006 | 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".