Bibliographic record
Abstract
Posing questions and critically examining the current state of affairs across the spectrum are essential to large-scale quality improvement. The questions we are about to pose are not intended to be gratuitously provocative; they must be addressed to get an accurate assessment of where the system is gridlocked, which interests are aligned or misaligned with a quality agenda and what measures must be taken to move forward. Moreover, the great majority of the questions will come as no surprise to those experienced with the system’s dynamics and frustrated by the inability to act. If we want to accelerate change and improve performance on a larger scale, we have to do things differently. We means the principal actors in the system – governments, regulatory agencies, organizations, boards and senior managers, professional groups etc. All have either legal or moral authority to demand and promote quality care; some have both. Collectively, we have the power to make healthcare accountable for quality and to implement policies and practices that are fully aligned with a quality agenda. A major obstacle to progress is the failure to ask ourselves the wicked questions that will lead to a deep exploration of assumptions we make. Without exploring our assumptions, we will continue to be hostage to our indifference to failure and be unable to reach our improvement potential. Exposing these assumptions can be both uncomfortable and a relief. It is uncomfortable because the conclusions we draw and the beliefs we adopt based on our assumptions often seem to be “the truth” – obvious, acceptable and defensible. They guide us to do and say “the right things.” By engaging people in dialogue, wicked questions invite exploration into inconsistencies in thought that have held us back from achieving our purpose, and can be used to promote a search for local solutions to organizational challenges. This opinion piece poses questions to the main protagonists.
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 | 0.064 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.096 |
| Scholarly communication | 0.022 | 0.048 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.014 | 0.040 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".