In pursuit of a valid information assessment method for continuing education: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: The Information Assessment Method (IAM) is a popular tool for continuing education and knowledge translation. After a search for information, the IAM allows the health professional to report what was the search objective, its cognitive impact, as well as any use and patient health benefit associated with the retrieved health information. In continuing education programs, professionals read health information, rate it using the IAM, and earn continuing education credit for this brief individual reflective learning activity. IAM items have been iteratively developed using literature reviews and qualitative studies. Thus, our research question was: what is the content validity of IAM items from the users' perspective? METHODS: A two-step content validation study was conducted. In Step 1, we followed a mixed methods research design, and assessed the relevance and representativeness of IAM items. In this step, data from a longitudinal quantitative study and a qualitative multiple case study involving 40 family physicians were analyzed. In Step 2, IAM items were analyzed and modified based on a set of guiding principles by a multi-disciplinary expert panel. RESULTS: The content validity of 16 IAM items was supported, and these items were not changed. Nine other items were modified. Three new items were added, including two that were extensions of an existing item. CONCLUSION: A content validated version of the IAM (IAM 2011) is available for the continuing education of health professionals.
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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 | 0.289 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".