Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Double checking is a standard practice in many areas of health care, notwithstanding the lack of evidence supporting its efficacy. We ask in this study: 'How do front line practitioners conceptualize double checking? What are the weaknesses of double checking? What alternate views of double checking could render it a more robust process?' METHOD: This is part of a larger qualitative study based on 85 semi-structured interviews of health care practitioners in general internal medicine and obstetrics and neonatology; thematic analysis of the transcribed interviews was undertaken. Inductive and deductive themes are reported. RESULTS: Weaknesses in the double checking process include inconsistent conceptualization of double checking, double (or more) checking as a costly and time-consuming procedure, double checking trusted as an accepted and stand-alone process, and double checking as preventing reporting of near misses. Alternate views of double checking that would render it a more robust process include recognizing that double checking requires training and a dedicated environment, Introducing automated double checking, and expanding double checking beyond error detection. These results are linked with the concepts of collective efficiency thoroughness trade off (ETTO), an in-family approach, and resilience. CONCLUSION(S): Double checking deserves more questioning, as there are limitations to the process. Practitioners could view double checking through alternate lenses, and thus help strengthen this ubiquitous practice that is rarely challenged.
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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 | 0.067 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 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".