Development and Content Validation of the Information Assessment Method for Patients and Consumers
Bibliographic record
Abstract
BACKGROUND: Online consumer health information addresses health problems, self-care, disease prevention, and health care services and is intended for the general public. Using this information, people can improve their knowledge, participation in health decision-making, and health. However, there are no comprehensive instruments to evaluate the value of health information from a consumer perspective. OBJECTIVE: We collaborated with information providers to develop and validate the Information Assessment Method for all (IAM4all) that can be used to collect feedback from information consumers (including patients), and to enable a two-way knowledge translation between information providers and consumers. METHODS: Content validation steps were followed to develop the IAM4all questionnaire. The first version was based on a theoretical framework from information science, a critical literature review and prior work. Then, 16 laypersons were interviewed on their experience with online health information and specifically their impression of the IAM4all questionnaire. Based on the summaries and interpretations of interviews, questionnaire items were revised, added, and excluded, thus creating the second version of the questionnaire. Subsequently, a panel of 12 information specialists and 8 health researchers participated in an online survey to rate each questionnaire item for relevance, clarity, representativeness, and specificity. The result of this expert panel contributed to the third, current, version of the questionnaire. RESULTS: The current version of the IAM4all questionnaire is structured by four levels of outcomes of information seeking/receiving: situational relevance, cognitive impact, information use, and health benefits. Following the interviews and the expert panel survey, 9 questionnaire items were confirmed as relevant, clear, representative, and specific. To improve readability and accessibility for users with a lower level of literacy, 19 items were reworded and all inconsistencies in using a passive or active voice have been solved. One item was removed due to redundancy. The current version of the IAM4all questionnaire contains 28 items. CONCLUSIONS: We developed and content validated the IAM4all in partnership with information providers, information specialists, researchers and representatives of information consumers. This questionnaire can be integrated within electronic knowledge resources to stimulate users' reflection (eg, their intention to use information). We claim that any organization (eg, publishers, community organizations, or patient associations), can evaluate and improve their online consumer health information from a consumers' perspective using this method.
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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 | 0.169 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".