Role of online health communities in patient compliance: a social support perspective
Bibliographic record
Abstract
Purpose Online health communities (OHC) can transform the healthcare industry, particularly in developing economies. Technology advancement and increased health literacy pave the way for these communities to become powerful tools for empowering patients. The purpose of this study was to empirically validate the linkages between social support and how it overarchingly influences patient compliance. Following social support theory, this study delineates how support from the community affects the patient–physician relationship (PERP) and consequently patient compliance regarding the treatment plan. This study also invents the role of patient trust in an OHC in moderating the relationship between PERP and engagement. Design/methodology/approach This paper is based on social support and empowerment theories to investigate the importance of social support in improving patients’ health behaviours and health outcomes via patient empowerment, patient engagement and patient compliance. The authors surveyed users from three Facebook cancer communities in India to collect data. The authors used partial least squares structured equation modelling and necessary condition analysis (NCA) with 265 participants to support the proposed model. Findings The result demonstrates that PERP is a crucial factor for patient engagement in OHC, and patient engagement has a significant effect on patient compliance. The results also showed that trust was a significant moderator between PERP and engagement. The NCA analysis shows all the relationships are significant; however, emotional support is not a necessary condition for PERP. Research limitations/implications By empowering cancer patients and enabling them to meet their emotional and informational needs through OHCs, the study model can aid in the development of solutions that will improve compliance with their treatment in an emerging economic context. The findings indicate the potential chain reaction of social support and PERP in online cancer health communities. This study also contributes to quantifying the social impacts of online healthcare services and how to enhance the healthcare compliance framework. Originality/value This study combines social support and empowerment theory with patient, physician, and technology to provide a fine-grained picture of PERP in OHC. It explains how social support in OHC promotes self-care behaviour. This linkage validation enables readers and the community at large to gain a more nuanced understanding of how social support – through PERP, engagement and trust – enables patient compliance using primary data.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".