Impact of Research-based Synopses Delivered as Daily E-mail: A Prospective Observational Study
Bibliographic record
Abstract
We conducted a prospective observational study to (1) determine usage and construct validity of a method to gauge the cognitive impact of information derived from daily e-mail, and (2) describe self-reported impacts of research-based synopses (InfoPOEMs) delivered as e-mail. Ratings of InfoPOEMs using an Impact assessment scale provided (a) data on usage of the impact assessment method, (b) reports of impact by InfoPOEM and by doctor and (c) data for analysis of construct validity of the scale. PARTICIPANTS were family physicians or general practitioners who rated at least five InfoPOEMs delivered on e-mail. For each InfoPOEM rated, 0.1 continuing education credit was awarded by the College of Family Physicians of Canada. Use of the impact assessment scale linked to a daily InfoPOEM was sustained during the 150-day study period. 1,007 participants submitted 61,493 reports of 'cognitive impact' by rating on average 61 InfoPOEMs (range 5-111). 'I learned something new' was most frequently reported. 'I was frustrated as there was not enough information or nothing useful' was the most frequently reported negative type of impact. The proportion of reports of 'No Impact' varied substantially across individual InfoPOEMs. Impact patterns suggested an 8 or 9-factor solution. Our Impact assessment method facilitates knowledge transfer by promoting two-way exchange between providers of health information and family doctors. Providers of health information can use this method to better understand the impact of research-based synopses. Sustaining current practice and increasing knowledge about new developments in medicine are important outcomes arising from research-based synopses delivered as e-mail, in addition to practice change.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".