E-mailed Evidence Based Summaries Impact Physician Learning More than Practice. A Review of: Grad, Roland M., Pierre Pluye, Jay Mercer, Bernard Marlow, Marie Eve Beauchamp, Michael Shulha, Janique Johnson-Lafleur and Sharon Wood-Dauphinee. “Impact of Research-based Synopses Delivered as Daily E-mail: A Prospective Observational Study.” Journal of the American Medical Informatics Association (2008)15.2: 240-5.
Bibliographic record
Abstract
Objective – To determine the use and construct validity of a method to assess the cognitive impact of information derived from daily e-mail evidence based summaries (InfoPOEMs), and to describe the self-reported impact of these InfoPOEMs. Design – Prospective, observational study over a period of 150 days employing a questionnaire and rating scale. Setting – This study was conducted via the Internet between September 8, 2006 and February 4, 2007. Subjects – Canadian Medical Association (CMA) members who received InfoPOEMs via e-mail as of September 2006 were invited to participate. For inclusion in the analyses, a participant was defined as a practising family physician or general practitioner who submitted at least five ratings of InfoPOEMS during the study period (n=1,007). Methods – Volunteers completed a demographic questionnaire and provided informed consent online. Each subsequent InfoPOEM delivered included a link to a “ten-item impact assessment scale” (241). Participants checked “all that apply” of descriptive statements such as: My practice was (will be improved); I learned something new; I think this information is potentially harmful. Each combination of selections made was considered a pattern of cognitive impact. College of Family Physicians of Canada received continuing medical education (CME) credit for each InfoPOEM rated. Data were collected by the CMA and forwarded weekly to the investigators who used descriptive statistics, principal component analysis, and multilevel factor analysis to analyze the data. Main Results – 1,007 participants rated an average of 61 InfoPOEMs (ranging from five to 111). A total of 61,493 patterns of cognitive impact were submitted. Eighty-five different patterns were observed, i.e., there were 85 different combinations of the scale’s statements used. Ten patterns accounted for 89.4% of the reports. The top five patterns were: I learned something new (35.2%); No impact (17.1%); This information confirmed I did (will do) the right thing (9.6%); I learned something new AND My practice will be improved (9.4%); and, I was reassured (5.6%). I disagree with this information was checked at least once by 10.3% of the participants, and 8.0% checked I think this information is potentially harmful at least once. Conclusion – The authors applied a cognitive assessment instrument to determine the impact of InfoPOEMs e-mailed to primary care physicians in Canada and found that ten combinations of impact descriptors accounted for 89.4% of the total reports. Most suggested a positive impact on knowledge or practice. Of the total, 17.1% indicated No impact and 1.8% indicated the participant was frustrated as there was not enough information or nothing useful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".