The experiential health information processing model: supporting collaborative web-based patient education
Bibliographic record
Abstract
BACKGROUND: First generation Internet technologies such as mailing lists or newsgroups afforded unprecedented levels of information exchange within a variety of interest groups, including those who seek health information. With emergence of the World Wide Web many communication applications were ported to web browsers. One of the driving factors in this phenomenon has been the exchange of experiential or anecdotal knowledge that patients share online, and there is emerging evidence that participation in these forums may be having an impact on people's health decision making. Theoretical frameworks supporting this form of information seeking and learning have yet to be proposed. RESULTS: In this article, we propose an adaptation of Kolb's experiential learning theory to begin to formulate an experiential health information processing model that may contribute to our understanding of online health information seeking behaviour in this context. CONCLUSION: An experiential health information processing model is proposed that can be used as a research framework. Future research directions include investigating the utility of this model in the online health information seeking context, studying the impact of collaborating in these online environments on patient decision making and on health outcomes are provided.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".