E-learning interventions are comparable to user's manual in a randomized trial of training strategies for the AGREE II
Bibliographic record
Abstract
BACKGROUND: Practice guidelines (PGs) are systematically developed statements intended to assist in patient and practitioner decisions. The AGREE II is the revised tool for PG development, reporting, and evaluation, comprised of 23 items, two global rating scores, and a new User's Manual. In this study, we sought to develop, execute, and evaluate the impact of two internet interventions designed to accelerate the capacity of stakeholders to use the AGREE II. METHODS: Participants were randomized to one of three training conditions. 'Tutorial'--participants proceeded through the online tutorial with a virtual coach and reviewed a PDF copy of the AGREE II. 'Tutorial + Practice Exercise'--in addition to the Tutorial, participants also appraised a 'practice' PG. For the practice PG appraisal, participants received feedback on how their scores compared to expert norms and formative feedback if scores fell outside the predefined range. 'AGREE II User's Manual PDF (control condition)'--participants reviewed a PDF copy of the AGREE II only. All participants evaluated a test PG using the AGREE II. Outcomes of interest were learners' performance, satisfaction, self-efficacy, mental effort, time-on-task, and perceptions of AGREE II. RESULTS: No differences emerged between training conditions on any of the outcome measures. CONCLUSIONS: We believe these results can be explained by better than anticipated performance of the AGREE II PDF materials (control condition) or the participants' level of health methodology and PG experience rather than the failure of the online training interventions. Some data suggest the online tools may be useful for trainees new to this field; however, this requires further study.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".