‘The problem with…’: a new series on problematic improvements and problematic problems in healthcare quality and patient safety
Bibliographic record
Abstract
Who has not attended an organisational meeting focused on some quality problem and not groaned in response to a suggestion of the type ‘We should just …have a new policy’, ‘…send out performance reports’, ‘… create a checklist’, ‘go after the low-hanging fruit’, or any of a number of other commonly suggested strategies for dealing with quality-related problems. Whether the groan occurs audibly or just internally depends on one's self-control and role in the organisation. Following the groan, one may even launch into a short speech beginning with the phrase ‘The problem with… new policies [or checklists or whatever the case may be] is…’ Whether this monologue occurs internally or externally again depends on one's self-control and willingness to risk alienating others at the meeting. With this editorial, we announce the launch of a new series of articles in BMJ Quality & Safety giving voice to these groans and monologues in response to frequently espoused but problematic improvement strategies, as well as problems that seem never to go away. Entitled ‘The problem with…’, each article will discuss controversial topics related to efforts to improve healthcare quality, including widely recommended but deceptively difficult strategies for improvement (‘problematic solutions’) and pervasive problems that seem to resist solution. Table 1 lists some example topics and briefly outlines the motivations for including them. We have commissioned some articles already, but encourage uninvited submissions as well (ideally in discussion with one of the editors before embarking on writing the full article). View this table: Table 1 Example topics and the basis for their inclusion Something can be difficult without being ‘problematic’. When we know what work needs to be done to achieve a goal, we knuckle down and do the work. That is not problematic. If we do not have the time or resources to invest in this work, we walk …
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".