Realising the potential of digital health communities: a study of the role of social factors in community engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health and fitness communities in the digital age are of strategic importance to global health and wellbeing. Despite this, information systems (IS) research on digital health and fitness communities has not kept pace with societal needs. Using partial least squares analysis, this study examines a number of social factors to predict members’ continuance intention (CI) in digital health and fitness communities. The findings confirm that the social presence (SP) of the community, as perceived by a member, influences their sense of belonging (SB) to it. SP and SB influence the member’s emotional engagement (EE) and appreciation of being recognised (AR) by the community. Subsequently, EE and AR are found to influence CI to stick with the community. In addition, the social influence (SI) of one’s social circles influences AR and CI. Departing from the dominant approaches, this study advances IS research on digital communities by conceptualising and testing a model to predict CI according to social relational theories. The study offers new theoretical foundations, which are appropriate to digital communities, upon which future studies can be based. Further, the findings offer practical insights for improving engagement in digital health and fitness communities.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it