Evaluation of email alerts in practice: part 1 – review of the literature on clinical emailing channels
Bibliographic record
Abstract
RATIONALE: Methods to systematically assess electronic knowledge resources by health professionals may enhance evaluation of these resources, knowledge exchange between information users and providers, and continuing professional development. We developed the Information Assessment Method (IAM) to document health professional perspectives on the relevance, cognitive impact, potential use and expected health outcomes of information delivered by (push) or retrieved from (pull) electronic knowledge resources. However, little is known about push communication in health sciences, and what we propose to call clinical emailing channels (CECs). CECs can be understood as a communication infrastructure that channels clinically relevant research knowledge, email alerts, from information providers to the inboxes of individual practitioners. AIMS: In two companion papers, our objectives are to (part 1) explore CEC evaluation in routine practice, and (part 2) examine the content validity of the cognitive component of IAM. METHODS: The present paper (part 1) critically reviews the literature in health sciences and four disciplines: communication, information studies, education and knowledge translation. Our review addresses the following questions. What are CECs? How are they assessed? RESULTS: The review contributes to better define CECs, and proposes a 'push-pull-acquisition-cognition-application' evaluation framework, which is operationalized by IAM. CONCLUSION: Compared with existing evaluation tools, our review suggests IAM is comprehensive, generic and systematic.
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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 | 0.063 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".