A Framework for Designing Inclusive \nOnline Communities \n \n \nThe Role of Inclusive Design for Salutogenesis in Chronic \nDisease Online Communities
Bibliographic record
Abstract
Online health communities are often designed for clinical purposes. The user needs within a chronic care community such as cancer are as diverse and complex as their symptom and treatment for latent and long-term effects. While these communities provide the \nfunctional needs such as synchronous and asynchronous communication features, they often fail \nto deliver a functional design that is inclusive of all user needs. The ability to inclusively \ndesign online health communities is critical to the overall goal of user satisfaction and in turn \nthe salutogenesis of the participants. The proactive approach to health and wellness can \nbe supported and influenced through online communities however; to ensure the broadest reach is \npossible to these communities they must be designed to be inclusive. This paper will define a tool \nby which online health communities can be designed and evaluated for access and participation while \nensuring the wide range of human diversity. The Framework for Inclusive Design of \nOnline Communities [FIDOC] will propose the key elements that are necessary to support \n the notion of well-being in these chronic care communities. FIDOC addresses the \nprocess by which designers can iteratively work to achieve inclusion when designing online \nhealth communities and offers \nrecommendations for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.015 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".