Bibliographic record
Abstract
Communication failure constitutes a key contributor to healthcare errors.1 ,2 In addition to poor communication and poor hand-offs, failure to speak up when one recognises a potential safety problem—unsafe acts or unprofessional behaviour—represents an important example of communication failure. Despite on-going calls for clinicians to speak up when they notice threats to patient safety, speaking up remains difficult due to fear of repercussions,3 ,4 power differences and authority gradients,1 ,5 among other factors. Some evidence suggests that clinicians may not speak up even when they perceive substantial potential for harm.6 In an effort to tackle the issue of speaking up, Martinez et al 7 present preliminary psychometrics for a new measure of speaking up climate . This paper makes an important contribution to the literature, as the field can certainly benefit from a measure that focuses on perceptions and enablers of ‘speaking up’. Martinez et al study two scales. The first measures the climate for speaking up about traditional patient safety concerns (SUC-Safe), such as improper sterile technique or an inadequate hand off. The second scale focuses on perceptions of speaking up about professionalism-related safety concerns (SUC-Prof), such as covering up an error, false documentation or disruptive behaviour. Both of these areas—traditional patient safety concerns and unprofessional behaviours—clearly represent important targets for ‘speaking up’ by members of the care team. Several of the findings from Martinez et al hold interest. Their results comparing per cent positive scores on their speaking up climate scales when compared with more general safety attitudes scores (measured using the Safety Attitudes Questionnaire8) highlight residents’ overall reluctance to speak up in general, but particularly regarding professionalism issues. These findings can drive change …
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".