Content validation of the information assessment method for delivery of educational material: a mixed methods study
Bibliographic record
Abstract
Context: The delivery of educational material constitutes an opportunity to engage Family Physicians (FPs) in online continuing education. The Information Assessment Method (IAM) allows FPs to report the clinical relevance, cognitive impact, use, and expected patient health benefits of clinical information received by email. About 10,000 Canadian physicians and pharmacists use IAM within accredited continuing education programs; however, IAM has not been fully content validated for the delivery of educational material. Objectives: Study the content validity of IAM (relevance and representativeness of IAM items) for the delivery of educational material. Design: Mixed methods convergent design. Quantitative part: Measure the relevance of IAM items. Participants and setting: Canadian Medical Association members who use IAM. Data collection: 234,194 ratings using IAM were collected in 2012. Data analysis: Descriptive statistics to calculate the relevance of IAM items with respect to their main construct. Qualitative part: Evaluate the representativeness of IAM items. Participants and setting: 15 FPs from McGill University. Data collection: Semi-structured face-to-face interview. Data analysis: Inductive-deductive thematic analysis to assess the representativeness of IAM items within each construct. Mixing Part: Results from quantitative and qualitative analyses were reviewed, combined, integrated and then discussed with experts. Results: The content validity of 21 items was confirmed, while two items were excluded. A new validated version was generated (IAM-2014). Conclusion: A tool to assess the clinical information received by physicians and pharmacists within online continuing education programs was validated. Consequently, information providers will be able to use valid results and feedback from FPs, to improve their product.
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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 | 0.262 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".