Behaviour Change and e-Health – Looking Broadly: A Scoping Narrative Review
Bibliographic record
Abstract
Behaviour change can refer to any transformation or modification of human behaviour. Within healthcare it refers to a broad range of activities and approaches that focus on the individual, community, or environmental influences on health-related behaviour. For e-Health (or digital health) it refers to behavioural impacts mediated through a specific e-Health intervention. However, there are also other health-related behaviour changes being quietly imposed upon both the populace and the healthcare professions broadly, by use of information and communications technologies for health. To better understand these deliberate or incidental impacts on the behaviour of healthcare consumers and providers alike, a scoping narrative review was performed using peer-reviewed and grey literature resources. Qualitative information was charted from the selected literature. This created an objective analysis of both contemporary and less commonly appreciated aspects of behaviour change in our 'digital' age. Many contemporary examples exist. The Internet and www brought alternate approaches moving from face-to-face or paper-based to websites, electronic diaries, and now mobile phones (particularly smartphones) to personalize health-related behaviour change in a myriad of diseases and conditions. Segments of the population have also exhibited health-related behaviour change through their growing www-based health-information seeking. More recent examples include 'spontaneous telemedicine' where physicians have changed the behaviour of themselves and colleagues through use of Instant Messaging, e.g., WhatsApp. Patients are also changing their behaviour spontaneously through taking and providing 'medical selfies'. However, the recent and rapid growth in accessibility and popularity of social media has markedly impacted behaviour change through the speed with which information can be spread, by both legitimate users and socialbots. Insidious examples include spread of health-related 'misinformation' (e.g., vaginal cleansing,), and now 'disinformation' (e.g., the 'anti-vaccination' movement, now resulting in recurrence of once eradicated diseases). These, and other examples, represent the broader, sometimes incidental, impact of some current e-health approaches on health-related behaviour change and should be identified and acknowledged as such. Doing so may fundamentally change opinion and efforts to redirect elements of behaviour change and aspects of behaviour change theory in unexpected ways.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".