The cognitive impact of research synopses on physicians: aprospective observational analysis of evidence-based summaries sentby email
Bibliographic record
Abstract
BACKGROUND: Effective information transfer in primary care is becoming more difficult as the volume of medical information expands. Emailed research synopses are expected to raise awareness and thereby permit more effective information retrieval. OBJECTIVE: To identify key factors that influence physicians' self-reported cognitive impact of emailed research synopses. METHOD: In this prospective observational study, research synopses sent by email between 8 September 2006 and 30 May 2007 were analysed. Seven characteristics of synopses (number of characters, research design, study setting, number of types of patient populations studied, number of comparisons, number of outcomes, and number of results) were analysed. Each synopsis was classified as either positive or negative based on physician-reported impacts. Logistic regression analysis was used to evaluate the association between a negative impact and the synopsis' characteristics. RESULTS: A total of 1960 Canadian physicians submitted 159,442 ratings on 193 synopses. Each synopsis was assessed on average by 826.1 physicians. On average there were 28.3 negative ratings per research synopsis, 146.3 neutral, and 656.2 positive. Out of the seven characteristics analysed, only the number of comparisons (odds ratio (OR) = 0.47, 95% confidence interval (CI) = 0.23-0.93) and the number of results (OR = 0.64, 95% CI = 0.44-0.93) had a statistically significant influence on physician ratings. An increase in the number of comparisons (P = 0.03) or the number of results (P = 0.02) decreased the likelihood of a negative impact. CONCLUSIONS: Characteristics of the synopses appear to influence cognitive impact, and there might be lexical patterns specific to these factors. Further research is recommended in order to understand the mechanism for the influence of these characteristics.
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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 | 0.022 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".