Silence in physician clinical practice: a scoping review protocol
Bibliographic record
Abstract
Abstract Objective The objective of this review is to map, describe and conceptualize how silence is discussed within literature on interactions between physicians and patients, in clinical settings. Methods We will use the methodological framework of Arksey & O’Malley, adapted by Levac et al and Joanna Briggs Institute. Empirical studies including quantitative, qualitative, mixed methods, observational studies and reviews will be included. Commentaries, editorials, and grey literature will also be examined. The databases MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Scopus and Web of Science will be searched. A two-part study selection strategy will be applied. First, reviewers will follow inclusion and exclusion criteria based on ‘Population-Concept-Context’ framework to independently screen titles and abstracts. Next, full texts will be screened. Data will be extracted, collated, and charted to summarize methods, outcomes and key findings from the articles included. Expected results and implications This scoping review will provide an extensive description of how physicians engage with silence in clinical settings. Findings will identify how silence is perceived in physician patient interactions, the roles it plays, what factors influence use of silence and guide development of educational initiatives on use of silence in clinical settings.
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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 | 0.125 | 0.093 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".