When Educational Material Is Delivered: A Mixed Methods Content Validation Study of the Information Assessment Method
Bibliographic record
Abstract
BACKGROUND: The Information Assessment Method (IAM) allows clinicians to report the cognitive impact, clinical relevance, intention to use, and expected patient health benefits associated with clinical information received by email. More than 15,000 Canadian physicians and pharmacists use the IAM in continuing education programs. In addition, information providers can use IAM ratings and feedback comments from clinicians to improve their products. OBJECTIVE: Our general objective was to validate the IAM questionnaire for the delivery of educational material (ecological and logical content validity). Our specific objectives were to measure the relevance and evaluate the representativeness of IAM items for assessing information received by email. METHODS: A 3-part mixed methods study was conducted (convergent design). In part 1 (quantitative longitudinal study), the relevance of IAM items was measured. Participants were 5596 physician members of the Canadian Medical Association who used the IAM. A total of 234,196 ratings were collected in 2012. The relevance of IAM items with respect to their main construct was calculated using descriptive statistics (relevance ratio R). In part 2 (qualitative descriptive study), the representativeness of IAM items was evaluated. A total of 15 family physicians completed semistructured face-to-face interviews. For each construct, we evaluated the representativeness of IAM items using a deductive-inductive thematic qualitative data analysis. In part 3 (mixing quantitative and qualitative parts), results from quantitative and qualitative analyses were reviewed, juxtaposed in a table, discussed with experts, and integrated. Thus, our final results are derived from the views of users (ecological content validation) and experts (logical content validation). RESULTS: Of the 23 IAM items, 21 were validated for content, while 2 were removed. In part 1 (quantitative results), 21 items were deemed relevant, while 2 items were deemed not relevant (R=4.86% [N=234,196] and R=3.04% [n=45,394], respectively). In part 2 (qualitative results), 22 items were deemed representative, while 1 item was not representative. In part 3 (mixing quantitative and qualitative results), the content validity of 21 items was confirmed, and the 2 nonrelevant items were excluded. A fully validated version was generated (IAM-v2014). CONCLUSIONS: This study produced a content validated IAM questionnaire that is used by clinicians and information providers to assess the clinical information delivered in continuing education programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".