A randomized trial to evaluate e-learning interventions designed to improve learner's performance, satisfaction, and self-efficacy with the AGREE II
Bibliographic record
Abstract
BACKGROUND: Practice guidelines (PGs) are systematically developed statements intended to assist in patient, practitioner, and policy decisions. The AGREE II is the revised and updated standard tool for guideline development, reporting and evaluation. It is comprised of 23 items and a user's Manual. The AGREE II is ready for use. OBJECTIVES: To develop, execute, and evaluate the impact of two internet-based educational interventions designed to accelerate the capacity of stakeholders to use the AGREE II: a multimedia didactic tutorial with a virtual coach, and a higher intensity training program including both the didactic tutorial and an interactive practice exercise component. METHODS: Participants (clinicians, developers, and policy makers) will be randomly assigned to one of three conditions. Condition one, didactic tutorial -- participants will go through the on-line AGREE II tutorial supported by a virtual coach and review of the AGREE II prior to appraising the test PG. Condition two, tutorial + practice -- following the multimedia didactic tutorial with a virtual coach, participants will review the on-line AGREE II independently and use it to appraise a practice PG. Upon entering their AGREE II score for the practice PG, participants will be given immediate feedback on how their score compares to expert norms. If their score falls outside a predefined range, the participant will receive a series of hints to guide the appraisal process. Participants will receive an overall summary of their performance appraising the PG compared to expert norms. Condition three, control arm -- participants will receive a PDF copy of the AGREE II for review and to appraise the test PG on-line. All participants will then rate one of ten test PGs with the AGREE II. The outcomes of interest are learners' performance, satisfaction, self-efficacy, mental effort, and time-on-task; comparisons will be made across each of the test groups. DISCUSSION: Our research will test innovative educational interventions of various intensities and instructional design to promote the adoption of AGREE II and to identify those strategies that are most effective for training. The results will facilitate international capacity to apply the AGREE II accurately and with confidence and to enhance the overall guideline enterprise.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".