Prescribers' Knowledge and Skills for Interpreting Research Results: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Appropriate medication prescribing may be influenced by a prescriber's ability to understand and interpret medical research. The objective of this review was to synthesize the research related to prescribers' critical appraisal knowledge and skills-defined as the understanding of statistical methods, biases in studies, and relevance and validity of evidence. METHODS: We searched PubMed and other databases from January 1990 through September 2015. Two reviewers independently screened and selected studies of any design conducted in the United States, the United Kingdom, or Canada that involved prescribers and that objectively measured critical appraisal knowledge, skills, understanding, attitudes, or prescribing behaviors. Data were narratively synthesized. RESULTS: We screened 1204 abstracts, 72 full-text articles, and included 29 studies. Study populations included physicians. Physicians' extant knowledge and skills were in the low to middle of the possible score ranges and demonstrated modest increases in response to interventions. Physicians with formal education in epidemiology, biostatistics, and research demonstrated higher levels of knowledge and skills. In hypothetical scenarios presenting equivalent effect sizes, the use of relative effect measures was associated with greater perceptions of medication effectiveness and intent to prescribe, compared with the use of absolute effect measures. The evidence was limited by convenience samples and study designs that limit internal validity. DISCUSSION: Critical appraisal knowledge and skills are limited among physicians. The effect measure used can influence perceptions of treatment effectiveness and intent to prescribe. How critical appraisal knowledge and skills fit among the myriad of influences on prescribing behavior is not known.
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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 | 0.076 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".